A More Robust Defensive Offering

Scientific Beta has designed a defensive offering in answer to investors' needs for a reduction in volatility compared to the cap-weighted index, as well as a protection of capital in bear markets. This is achieved through the Smart Beta 2.0 construction framework, which first selects stocks with low volatility, then applies a High-Factor-Intensity (HFI) filter to remove stocks with the lowest multi-factor scores, and finally diversifies away idiosyncratic risks with a diversified weighting scheme. This approach delivers high factor intensity and good long-term risk-adjusted performance, since it harvests the Low Volatility factor which is known to provide an additional source of performance to the cap-weighted index over the long-term, while maintaining positive exposures to other rewarded risk factors through the use of the HFI filter. Moreover, the Scientific Beta top-down approach gives investors the flexibility to select the solution that best fits with their investment objectives by offering three different versions of defensive indices.

The Scientific Beta defensive offering relies on three types of indices in response to investors' varying objectives:

1. High-Factor-Intensity Low Volatility Diversified Multi-Strategy (4-Strategy)
2. High-Factor-Intensity Low Volatility Diversified Multi-Strategy (4-Strategy) (Sector Neutral)
3. Narrow High-Factor-Intensity Low Volatility Diversified Multi-Strategy (4-Strategy)

The High-Factor-Intensity Low Volatility Diversified Multi-Strategy (4-Strategy) index offers good exposure to the Low Volatility factor and hence a good level of volatility reduction and protection in bear markets, similar to popular benchmarks such as the MSCI Minimum Volatility index, while providing the highest factor intensity as well as the best risk-adjusted performance of our offering. This index is recommended for defensive investors with weak tracking error constraints who seek a solution that is not only defensive but that is also properly exposed to other rewarded risk factors to obtain the highest risk-adjusted return.

The High-Factor-Intensity Low Volatility Diversified Multi-Strategy (4-Strategy) (Sector Neutral) index offers the lowest volatility reduction and protection in bear markets. Moreover, it delivers the lowest Sharpe ratio of our offering. Nonetheless, its additional objective is also to reduce tracking error through the sector neutrality objective. This objective is achieved, since the index delivers the lowest tracking error and the best information ratio of our offering. Furthermore, it has low conditionality to market and macroeconomic factors, in particular to T-Bills and Term Spread factors. This index is recommended for defensive investors with tracking error constraints wishing to avoid negative relative performance in bull market regimes or in rallies of some sectors and who are worried by rising interest rates.

The Narrow High-Factor-Intensity Low Volatility Diversified Multi-Strategy (4-Strategy) index has the highest exposure to the Low Volatility factor and therefore delivers the strongest volatility reduction and offers the best protection in bear markets. The index is therefore designed for investors who seek the most defensive solution. Obviously, this high exposure to the Low Volatility factor comes at a cost, in the form of lower exposures to other rewarded risk factors, higher conditionality to various regimes meaning important relative losses in bull markets for instance, and high tracking error. Moreover, it has the strongest sensitivity to macroeconomic factors of our offering and in particular to T-Bills and Term Spread factors. For Scientific Beta, this index should not be considered as a standalone solution, but rather as an overlay solution for investors willing to modify their portfolio's market beta or Low Volatility exposure, while avoiding the reduction of the factor intensity of their existing portfolio through the HFI filter.

To conclude, Scientific Beta's defensive offering is motivated by a strong belief that investors are not identical and that their investment objectives and constraints are different. This is why we believe that our top-down approach, which is simple and transparent, is the best approach for our clients. Finally, we offer risk control options, such as a sector neutrality objective and concentrated selection in the form of narrow high factor intensity, that allow investors to define explicitly their preferences in terms of relative risks and the level of defensiveness which are often hidden by-products in defensive solutions offered by competitors. Obviously, whatever the defensive index chosen, the fact that they are part of the Scientific Beta smart factor indices means that they benefit from the same features as all the other indices that we offer, namely the good diversification of unrewarded risks and the capacity to limit undesired risks. For investors, this is the guarantee that their choice will be the best possible.

Key Statistics of Scientific Beta's Defensive Offering Compared to the MSCI Minimum Volatility Index

21/06/2002 to 31/12/2018 (RI/USD)	HFI Low Volatility Diversified Multi-Strategy (4S)	HFI Low Volatility Diversified Multi-Strategy (4S) (Sector Neutral)	Narrow HFI Low Volatility Diversified Multi-Strategy (4S)	MSCI Minimum Volatility
Panel A – SciBeta USA
Ann. Rel. Returns	2.7%	2.5%	1.7%	0.7%
Volatility Reduction	-21%	-15%	-25%	-17%
Sharpe Ratio Improvement	74%	60%	67%	33%
Protection in Bear Markets	10.8%	7.2%	14.0%	10.4%
Bull/Bear Mkt Rel. Return Spread	-16.0%	-9.7%	-23.6%	-18.7%
Factor Intensity	0.65	0.52	0.65	0.19
Ann. Tracking Error	6.0%	4.6%	7.5%	5.2%
Information Ratio	0.44	0.54	0.23	0.14
Term Spread Exposure	-1.48	-0.46	-1.80	-2.13
Panel B – SciBeta Developed
Ann. Rel. Returns	3.2%	2.8%	2.8%	1.0%
Volatility Reduction	-22%	-18%	-26%	-26%
Sharpe Ratio Improvement	96%	77%	96%	56%
Protection in Bear Markets	11.2%	8.5%	14.1%	14.1%
Bull/Bear Mkt Rel. Return Spread	-17.0%	-12.4%	-23.5%	-26.9%
Factor Intensity	0.65	0.56	0.60	0.26
Ann. Tracking Error	4.9%	3.9%	5.9%	6.0%
Information Ratio	0.66	0.72	0.47	0.16
Term Spread Exposure	-1.29	-0.73	-1.63	-2.39

The analysis is based on daily total returns in USD from 28/06/2002 (base date of Scientific Beta indices) to 28/12/2018. All statistics are annualised and regressions are based on weekly total returns in USD. The yield differential of Secondary US Treasury Bills (3M) is used as a proxy for the T-Bill Factor. The Term Spread factor is the difference in yield differential of 10-year US Treasury Bonds and yield differential of 3-year US Treasury Bonds. Coefficients significant at 5% p value are highlighted in bold. The smart factor indices used are the SciBeta USA High-Factor-Intensity Low Volatility Diversified Multi-Strategy (4-Strategy), SciBeta USA High-Factor-Intensity Low Volatility Diversified Multi-Strategy (4-Strategy) (Sector Neutral), SciBeta USA Narrow High-Factor-Intensity Low Volatility Diversified Multi-Strategy (4-Strategy), SciBeta Developed High-Factor-Intensity Low Volatility Diversified Multi-Strategy (4-Strategy), SciBeta Developed High-Factor-Intensity Low Volatility Diversified Multi-Strategy (4-Strategy) (Sector Neutral) and the SciBeta Developed Narrow High-Factor-Intensity Low Volatility Diversified Multi-Strategy (4-Strategy). The cap-weighted indices are the SciBeta USA Cap-Weighted and the SciBeta Developed Cap-Weighted.

A More Robust Defensive Offering, Scientific Beta white paper, February 2019

Towards Cost Transparency: Estimating Transaction Costs for Smart Beta Strategies

Surprisingly, little is known about the magnitude of transaction costs of smart beta indices. This is surprising, since such indices naturally face higher implementation challenges than cap-weighted market indices. Clearly, higher transaction costs are a likely "side-effect" of smart beta strategies that investors need to understand. However, such indices are commonly analysed as backtests of paper portfolios, ignoring real life transaction costs. Index providers sometimes state that adjusting the performance of their indices for transaction costs is not something that they can do easily and they prefer to leave it to market participants to figure out what the shortfall would be. This is curious. We would not imagine that pharmaceutical companies advertise drugs while saying that it is up to patients to figure out what the side-effects are.

In the rare cases where cost estimates of smart beta strategies are provided, cost estimates are based on proprietary transaction cost data or unreliable extrapolation from small samples. For investors, it is thus impossible to obtain a transparent and replicable comparison of cost estimates across different index strategies.

We believe that the current state of affairs is not satisfactory and that it is possible to improve investors' awareness of this topic by exploiting recent advances in the academic literature on microstructure to provide estimates of costs that draw on easily available public data and are not computationally intensive. Our estimates are thus transparent and easily replicable by investors or other third parties.

In this article, we use cost estimates to measure the net outperformance of investable smart beta indices in different regional markets with different levels of liquidity. We also draw on our cost estimates to analyse the main drivers of transaction costs.

We analyse transaction costs for Scientific Beta indices that are invested in the most liquid universes of stocks and that adopt turnover controls and liquidity rules to limit trading. Our results show that the outperformance of smart beta strategies relative to the cap-weighted benchmark is robust in accounting for transaction costs. Transaction costs range from 5 to 15 basis points per annum for Scientific Beta multi-factor indices across most Developed regions. Higher cost levels in the range of 30 to 40 basis points are observed in Developed Asia-Pacific ex Japan indices and Emerging market indices. These cost estimates are an order of magnitude smaller than the gross outperformance of these strategies. As a result, relative returns and information ratios are still high and positive when assessing performance net-of-cost. We also find positive net-of-cost performance in the US universe when considering a longer time period with higher cost levels than over the recent period. Finally, we show that geographic regions are the main driver of cost variation and that investors who want to diversify away a rising transaction costs shock may benefit from diversification across regions.

We argue that replicable methods to estimate transaction costs should be adopted widely to increase the transparency of smart beta implementation costs for investors.

Full article

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Measuring Factor Exposure Better to Manage Factor Allocation Better

A webinar hosted by Felix Goltz, Research Director at Scientific Beta, on 15 November, 2018 presented the issues related to factor risk measurement and showed how these can be countered.

Factor investing offers a big promise. By identifying the persistent drivers of long-term returns in their portfolios, investors can understand which risks they are exposed to, and make explicit choices about those exposures.

When it comes to information about factors, providers offer analytic toolkits to identify the factor exposures of an investor's portfolio. However, these analytic tools do not employ academically grounded factors and their factor finding process maximises the risk of ending up with false factors. These non-standard factors also lead to mismeasurement of exposures and may capture exposure to redundant factors. In the end, analytic tools for investors do not deliver on the promise of factor investing and they also lack transparency.

Additionally, we may question the way in which the measurement of factor proxies is implemented. Most popular factor analysis tools used by investors deviate from the models used in research because they choose to use factor scores instead of betas. An additional problem is that the one-dimensional nature of factor scores does not take into account correlations across different factors. This leads to the double counting of the exposures of factors that are highly correlated. Lastly, many popular factor scores combine variables into composite factor scores. Combining factor scores into composite scores makes the mismeasurement problems worse as composites from skewed score distributions may be biased towards one of the variables.

This webinar reviewed these issues of factor risk measurement and showed how these can be countered.

Measuring Factor Exposure Better to Manage Factor Allocation Better: A Critical Approach to Popular Factor Box Initiatives, Scientific Beta white paper, October 2018

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Managing Sector Risk in Factor Investing

Explaining the benefits of applying sector neutrality, this webinar, hosted by Eric Shirbini, Global Research and Investment Solutions Director at Scientific Beta, and Daniel Aguet, Deputy Research Director of Scientific Beta, on 18 December, 2018 reviews the sector risk control option offered to investors by Scientific Beta.

Sector risk is an implicit bet investors take when investing in smart factor indices. Even if it is not a priced risk factor in the cross-section of expected returns, sector risk can nevertheless have a material impact on short-term performance.

In a new publication entitled "Managing Sector Risk in Factor Investing", Scientific Beta researchers focus on the implicit sector risk taken by smart factor indices and analyse the implications for their short- and long-term risk-adjusted performance. Investors looking to manage short-term risks can use the sector-neutral risk control option offered on Scientific Beta indices. Using the sector-neutral risk option has a clear advantage in terms of relative risk-adjusted performance since information ratios are increased.

This webinar explains the benefits of applying sector neutrality and reviews the sector risk control option offered to investors by Scientific Beta.

Managing Sector Risk in Factor Investing, Scientific Beta white paper, November 2018

Interview with Noël Amenc, CEO of Scientific Beta, winner of the "Indexing Firm of the Year 2019" Risk Award

In this interview, Noël Amenc, CEO of Scientific Beta and Professor of Finance at EDHEC Business School, provides his thoughts following the recent presentation of the prestigious Risk Award for "Indexing Firm of the Year 2019" to Scientific Beta, highlights the importance of risk management in the factor space and discusses the organisation's current situation and future projects.

Scientific Beta won the Risk Award for “Indexing Firm of the Year 2019” at the annual awards ceremony that was held in London at the end of the year. What is your reaction to this award?

The Risk Awards are the longest-running and most prestigious awards for firms and individuals involved in the global derivatives markets and in risk management. It is particularly pleasing for Scientific Beta to be recognised for its work in the area of risk management for smart beta indices, because this is something that we have always prioritised in the development of our indices. Our view is that investors have a fiduciary responsibility to manage risk and our role and duty is to provide them with appropriate risk diversification and non-factor risk-control mechanisms such as our market-beta-adjustment and sector-neutral options. We think that the search for smart beta outperformance should not sacrifice the sound risk management of such strategies and it is probably this sound risk management that allows an index or a factor or alternative weighting strategy to be qualified as 'smart'.

Many providers speak about risk management. How is Scientific Beta's proposition different in this area?

When we published an article in the Journal of Portfolio Management in 2012 entitled "Choose Your Betas," the whole of the smart beta industry referred to smart beta as alternative weightings that were smarter than cap weighting and provided access to better diversification or even to the representation of the true economic footprint, without this representation being subject to serious statistical tests. At the time, we were the first to say that the explicit choice of a weighting scheme had implicit, but very significant, consequences on the risk factors to which the portfolio or index that represented this choice of weighting was exposed. As such, we were the first to offer the possibility of making explicit risk factor exposure and weighting choices as part of what we called the smart beta 2.0 approach. I see that six years later the market leaders are joining us and are starting to offer this combination.

As early as 2012, our multi-beta indices were also offered with important risk-control options like sector neutrality. We felt that it was important in a long-only universe where the factor choices have strong sector consequences to make the consequences of these implicit factor risks explicit and to provide the possibility to reduce these sector deviations as part of a sector-neutrality option for investors who wished to do so. Naturally, every risk management constraint and risk hedging has consequences on the investment objective. For example, imposing a sector-neutrality constraint will necessarily lead to a reduction in the achievement of a factor intensity, or even volatility reduction, objective, and it is the investor's responsibility to make this fiduciary choice. An index provider is only an auxiliary to the asset manager or the investor, but this auxiliary can be more useful or less useful depending on the degree of transparency on the risks to which the index methodology exposes the investor or not. I think that this is what made Scientific Beta successful from the start. With our analytics platform that was freely accessible to all our clients and our risk management options we were able to speak the truth about the risks and the consequences of managing them or the choice of not taking them into account.

In the same way, when we published an article in 2017 on the importance of the market beta gap in the performance and volatility of long-only smart beta strategies, we think that we addressed an important point that was overlooked by factor investing specialists who, in their tendency to focus solely on the exposure to rewarded factors, forgot that in the long-only space the main source of the performance and volatility of an equity portfolio or index is its exposure to market risk. Here again, being aligned with the market beta or not has advantages and disadvantages. In the former case, the excess return will be higher over the long run, but the volatility will be higher too. We think that it is not our role to choose for the clients or to show them simulated track records for periods that are favourable or unfavourable with respect to the methodological choices. It is up to the investors, or to the asset managers to whom they delegate part of their fiduciary responsibilities, to make these choices.

Finally, in certain cases, when we think that there is no choice to be made and that some implicit risks are not justified in a factor investing framework, we make the decision to impose constraints. This is the case for example with our choice of regional neutrality in the construction of global or multi-region indices. We feel it is totally irresponsible to lead investors into regional choices for which the risk and performance are related to macroeconomic considerations on the basis of microeconomic criteria like those that the traditional factors define. On the pretext that the book-to-market of the Japanese market is cheaper, how could we accept that a world value index should be considerably overweight in Japanese stocks? If investors wish to increase their exposure to the Japanese market it is up to them to do so on the basis of their economic analysis and not on the basis of the unexpected consequences of a world or global multi-factor or value index methodology that they are offered.

I sincerely believe that this approach of documenting and managing the implicit risks of factor investing made us successful with respect to competitors who still favour anecdotes or displaying performance to justify their value-add over samples that are always limited.

Could you give us some background on Scientific Beta's situation at present?

Scientific Beta launched six years ago with the mission of maximising the impact of EDHEC Business School’s academic research in the indexing space. The organisation started with 12 people; now it has 52 and is hiring more. Scientific Beta's assets under replication are USD 43bn as of December 31, 2018. Scientific Beta has a dedicated team that covers not only client support from Nice and Singapore, but also the development, production and promotion of its index offering. We market our indices across the globe and have clients in Asia, Europe, the United States and Canada. Our geographical positions are consistent with our clients' locations: we have operations in Singapore, Boston and London covering all aspects of business development. We also have a representative office in Japan, as we have a strategic partnership with Nomura, a major Japanese asset management company. Our first success was in North America: Americans love innovation and, as such, are "early adopters". Up to 50% of our revenues are generated in the United States. Europe is now following suit. This is something of a paradox, since we were founded in Europe but our first clients were American and now Europe is following in their footsteps with around 40% market share. The rest relates to the "rest of the world", which represents slightly under 10%. Scientific Beta signed the United Nations-supported Principles for Responsible Investment (PRI) on September 27, 2016 and for us, in addition to this signature, we consider ESG and Low Carbon to be very important objectives for investors, objectives that we reconcile with the factor investing approaches. Today, more than 35% of the USD 43bn in assets under replication replicate indices with clear and stated ESG or Low Carbon objectives.

Scientific Beta is part of a business school and this is a subject that you highlight extensively in your communication. How is that relevant for your clients?

All major financial institutions, asset managers and index providers refer to research. Some do not hesitate to call themselves "research houses," but only EDHEC is really a research house whose activities in that area are regularly audited by independent academic accreditation organisations. We do indeed think that research should drive innovation in finance, but a serious research attitude consists not only of accumulating favourable or attractive results to demonstrate the usefulness of the innovation, but also of showing its limitations and documenting the robustness. I think that it is only on this condition that one can speak of a research house. This is the natural discipline in the academic world and it is this discipline that we have implemented as part of our Scientific Beta activity. We attach more importance to the robustness of the performance of the indices than to the level of in-sample performance.

What does the future hold for Scientific Beta?

What’s next for Scientific Beta? The first thing is rising to the challenge of climate change. I believe that generating sales gives personal satisfaction but doesn't contribute to social well-being. Just as EDHEC looks beyond its revenues to engage in a much more important mission – the future of students – we must also play our part in this future by protecting them from climate change. Finance can be very influential in combating climate change. Investors can influence decisions made not only by companies but also by governments and that is our next step. We will be supporting many avenues of research so as to develop the capabilities to construct indices that will be truly instrumental in combating climate change.

More on the Risk Award

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Noël Amenc: Noël Amenc, PhD, is Professor of Finance and Associate Dean for Business Development at EDHEC Business School and the founding Chief Executive Officer of Scientific Beta. His concern for bridging the gap between university and industry has led him to pursue a double career in academe and business. Prior to joining EDHEC Business School as founding director of EDHEC Risk Institute, he was the Director of Research of Misys Asset Management Systems, having previously created and developed a portfolio management software company. He has published numerous articles in finance journals as well as four books on quantitative equity management, portfolio management, performance analysis, and alternative investments. He is a member of the editorial board of the Journal of Portfolio Management, associate editor of the Journal of Alternative Investments, and member of the advisory board of the Journal of Index Investing. He is also a member of the Finance Research Council of the Monetary Authority of Singapore. He was formerly a member of the Consultative Working Group of the European Securities and Markets Authority (ESMA) Financial Innovation Standing Committee and of the Scientific Advisory Council of the AMF (French financial regulatory authority). He holds graduate degrees in economics, finance and management and a PhD in finance.

Scientific Beta has forged partnerships with asset managers who not only replicate its indices but also propose open funds enabling investors to readily invest in the strategies proposed by Scientific Beta that they have selected. In this issue, we focus on Legal & General Investment Management.

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Legal & General Investment Management

In November 2018, Brunel Pension Partnership, the Local Government Pension Scheme (LGPS) in the UK managing investment of the pension assets for the funds of Avon, Buckinghamshire, Cornwall, Devon, Dorset, Environment Agency, Gloucestershire, Oxfordshire, Somerset, and Wiltshire Funds, confirmed a major investment of almost £1bn in the Legal & General Investment Management Ltd (LGIM) Diversified Multi-Factor Equity Fund backed by Scientific Beta indices.

Brunel announced the appointment of LGIM as its passive equity fund manager in April 2018 and has since been transitioning assets to the manager. The LGIM Diversified Multi-Factor Equity Fund, launched in July 2017 as a commingled life fund for UK institutional clients, allocates between Scientific Beta indices according to regional weights determined by LGIM. The Scientific Beta High-Factor-Intensity Multi-Beta Multi-Strategy EW indices are custom indices that provide strong exposure to the rewarded risk factors (Low Volatility, Value, Low Investment and High Profitability) and a good level of diversification using an equal-weighted combination of the Maximum Deconcentration and Diversified Risk Weighted approaches:

• SciBeta Developed America High-Factor-Intensity Multi-Beta (val, vol, pro/inv) Multi-Strategy (Max Deconc, DRW)
• SciBeta United Kingdom High-Factor-Intensity Multi-Beta (val, vol, pro/inv) Multi-Strategy (Max Deconc, DRW)
• SciBeta Developed EMEA ex-UK High-Factor-Intensity Multi-Beta (val, vol, pro/inv) Multi-Strategy (Max Deconc, DRW)
• SciBeta Japan High-Factor-Intensity Multi-Beta (val, vol, pro/inv) Multi-Strategy (Max Deconc, DRW)
• SciBeta Developed Asia Pacific ex-Japan High-Factor-Intensity Multi-Beta (val, vol, pro/inv) Multi-Strategy (Max Deconc, DRW)
• SciBeta Emerging High-Factor-Intensity Multi-Beta (val, vol, pro/inv) Multi-Strategy (Max Deconc, DRW)

"We looked at a range of approaches to multi factor smart beta investing and believe that the LGIM Diversified Multi-factor Equity fund product which incorporates factor data and research by Scientific-Beta has several distinct advantages, including relatively simple construction, purity of approach and excellent track record," said Mark Mansley, CIO at Brunel Pension Partnership. "These are combined in a sensible way which avoids many of the issues of some multi-factor approaches. We regard it as a positive that LGIM has been actively involved in developing this product with Scientific Beta."

"Brunel Pension Partnership's investment in the LGIM Diversified Multi-factor Equity fund is a major vote of confidence in our approach of offering exposure to long-term rewarded risk factors, ensuring a good reward for these factors through good diversification of unrewarded risk, and guaranteeing sound risk management of the investment by implementing risk allocation between well-diversified factor indices," commented Noël Amenc, CEO at Scientific Beta. "We are delighted to see that this philosophy is appealing to one of the major institutional investors in the UK."

Scientific Beta Smart Factor Indices Performance

Scientific Beta offers smart factor indices that provide exposure to the six well known rewarded factors (Mid-Cap, Value, High Momentum, Low Volatility, High Profitability, and Low Investment) and which are also well diversified in order to reduce the specific risks. These indices are available in a wide range of versions, notably enabling broad and narrow indices to be distinguished that correspond to more or less pronounced choices of factor exposure. In addition, these single smart factor indices benefit from a High Factor Intensity Filter that allows them to be used in a multi-factor allocation by taking into account the negative interactions between factors. In this report, we have chosen to present the smart factors represented by the Scientific Beta Narrow High Factor Intensity Diversified Multi-Strategy (4-Strategy) indices.

Performance Overview

The following table displays the short-term, mid-term and long-term performance of Narrow High Factor Intensity Diversified Multi-Strategies (4-Strategy) by factor tilt in the Developed equity universe, which is the global universe managed by Scientific Beta. The six rewarded tilts selected (Mid-Cap, Value, High Momentum, Low Volatility, High Profitability, and Low Investment) are part of the common tilts documented in the literature as liable to produce outperformance compared to cap-weighted indices.

Narrow High Factor Intensity Diversified Multi-Strategy (4-Strategy) Index	Past Quarter	1 Year	5 Years	10 Years	Long-Term US Track Records 30/12/1977 to 29/12/2017 (40 years)
	Annualised Relative Return Compared to Broad Cap-Weighted as of 31/12/2018
Mid-Cap	-0.19%	-1.54%	1.79%	2.61%	3.12%
Value	0.61%	-0.92%	1.67%	1.87%	2.31%
High Momentum	-3.11%	-4.33%	0.78%	1.20%	3.08%
Low Volatility	4.84%	3.10%	2.19%	0.91%	1.99%
High Profitability	0.40%	2.33%	3.21%	3.28%	3.16%
Low Investment	-0.69%	-0.97%	1.40%	1.69%	2.80%

The history of Scientific Beta Index returns begins on 21/06/2002. The statistics are based on daily total returns (with dividends reinvested). All statistics are annualised, except for the past quarter relative returns and year-to-date relative returns which are non-annualised. The performance ratios that involve the average returns are based on the geometric average, which reliably reflects multiple holding period returns for investors. Scientific Beta uses the yield on Secondary Market US Treasury Bills (3M) as a proxy for the risk-free rate in US Dollars. All results are in USD as of 31/12/18 for the Developed universe.

During the last quarter of 2018, the performance of Scientific Beta smart factor indices ranged from -3.11% for the SciBeta Developed Narrow High-Factor-Intensity High-Momentum Diversified Multi-Strategy (4-Strategy) index to 4.84% for the SciBeta Developed Narrow High-Factor-Intensity Low-Volatility Diversified Multi-Strategy (4-Strategy) index compared to broad cap-weighted indices. Half of the factors outperformed their cap-weighted benchmark this quarter, with the six indices showing an average outperformance of 0.31%. In 2018, two of the six factors, Low Volatility and High Profitability, outperformed their cap-weighted benchmark.

Over the past ten years, all strategies posted positive relative returns in relation to broad cap-weighted indices, with values ranging from 0.91% for the SciBeta Developed Narrow High-Factor-Intensity Low-Volatility Diversified Multi-Strategy (4-Strategy) index to 3.28% for the SciBeta Developed Narrow High-Factor-Intensity High-Profitability Diversified Multi-Strategy (4-Strategy) index.

Scientific Beta Multi Smart Factor Indices Performance

Many investors choose to diversify their factor exposure so as not to be exposed to variations in the performance of a single factor. For this reason, Scientific Beta Multi-Beta Multi-Strategy (MBMS) indices provide an allocation to well-rewarded smart factor indices. Here again, Scientific Beta proposes a wide range of Multi-Beta Multi-Strategy indices based on the same Smart Beta 2.0 investment philosophy. This section presents those indices that enable the diversification of factor and specific risks to be reconciled. Among these indices, we have chosen to present some of the more popular ones, namely the strategy with the longest live track record – the Scientific Beta Multi-Beta Multi-Strategy Four-Factor Equal-Weight index and a strategy created more recently which takes into account the interactions between single-factor indices in order to provide higher factor intensity at a multi-factor level – represented by the Scientific Beta High-Factor-Intensity Diversified Multi-Beta Multi-Strategy 6-Factor 4-Strategy Equal-Weight index and its Sector Neutral, Market Beta Adjusted (Overlay) and combined Sector Neutral/Market Beta Adjusted (Overlay) versions.

Performance Overview

The table below displays an overview of the relative and absolute performance of Scientific Beta Multi-Beta Multi-Strategy indices for the Developed, United States and Global regions over different time periods.

Region	Multi-Beta Multi-Strategy Index	Nº Constituents	Relative Return Compared to Cap-Weighted			Information Ratio	Absolute Return	Volatility	Sharpe Ratio
			Past Quarter	YTD	10 Years	10 Years	10 Years	10 Years	10 Years
Developed	4-Factor EW	1749	0.05%	-1.37%	1.10%	0.45	11.21%	13.04%	0.83
	HFI 6-Factor 4-Strategy EW	1183	0.70%	-0.46%	2.19%	0.73	12.30%	12.42%	0.96
	HFI 6-Factor 4-Strategy EW Sector Neutral	1204	-0.60%	-0.26%	1.93%	0.78	12.03%	12.84%	0.91
	HFI 6-Factor 4-Strategy EW Market Beta Adjusted (Overlay)	1183	-0.73%	-1.52%	3.75%	1.76	13.85%	14.42%	0.94
	HFI 6-Factor 4-Strategy EW Sector Neutral Market Beta Adjusted (Overlay)	1204	-1.74%	-1.12%	3.14%	1.71	13.24%	14.54%	0.89
SciBeta Developed CW		1873					10.10%	14.34%	0.68
United States	4-Factor EW	470	0.25%	-2.07%	0.39%	0.13	13.36%	15.52%	0.84
	HFI 6-Factor 4-Strategy EW	317	1.01%	-1.44%	1.33%	0.36	14.30%	14.64%	0.95
	HFI 6-Factor 4-Strategy EW Sector Neutral	319	-0.86%	-0.84%	1.38%	0.48	14.35%	15.46%	0.91
	HFI 6-Factor 4-Strategy EW Market Beta Adjusted (Overlay)	317	-0.77%	-2.36%	3.16%	1.04	16.14%	16.68%	0.95
	HFI 6-Factor 4-Strategy EW Sector Neutral Market Beta Adjusted (Overlay)	319	-2.16%	-1.52%	2.63%	1.01	15.60%	16.99%	0.90
SciBeta United States CW		500					12.97%	16.49%	0.76
Global	4-Factor EW	2390	0.18%	-0.96%	1.13%	0.46	11.08%	12.72%	0.84
	HFI 6-Factor 4-Strategy EW	1618	0.70%	-0.21%	2.29%	0.77	12.24%	12.16%	0.98
	HFI 6-Factor 4-Strategy EW Sector Neutral	1641	-0.41%	0.10%	2.05%	0.83	11.99%	12.56%	0.93
	HFI 6-Factor 4-Strategy EW Market Beta Adjusted (Overlay)	1618	-0.73%	-1.37%	3.83%	1.90	13.78%	14.18%	0.95
	HFI 6-Factor 4-Strategy EW Sector Neutral Market Beta Adjusted (Overlay)	1641	-1.52%	-0.82%	3.27%	1.89	13.22%	14.31%	0.90
SciBeta Global CW		2565					9.95%	14.17%	0.68

Based on daily total returns in USD as of 31/12/2018. The base date is 21/06/2002 for Scientific Beta Multi-Beta Multi-Strategy 4-Factor EW indices, Scientific Beta High-Factor-Intensity Diversified Multi-Beta Multi-Strategy 6-Factor 4-Strategy EW indices and Scientific Beta High-Factor-Intensity Diversified Multi-Beta Multi-Strategy (Sector Neutral) 6-Factor 4-Strategy EW indices, 18/06/2004 for Scientific Beta High-Factor-Intensity Diversified Multi-Beta Multi-Strategy 6-Factor 4-Strategy EW Market Beta Adjusted (Overlay) indices and Scientific Beta High-Factor-Intensity Diversified Multi-Beta Multi-Strategy (Sector Neutral) 6-Factor 4-Strategy EW Market Beta Adjusted (Overlay) indices and 19/12/2003 for Scientific Beta Multi-Beta Multi-Strategy CW indices. All statistics are annualised, except for the past quarter relative returns and year-to-date relative returns which are non-annualised. Performance ratios that involve the average returns are based on the geometric average, which reliably reflects multiple holding period returns for investors. The risk-free rates used are defined according to the regional universe of the index. The number of index constituents are as of the last quarterly rebalancing, i.e. 21/12/2018.

During the last quarter of 2018, relative returns for the above indices varied from -2.16 % for the SciBeta United States High-Factor-Intensity Diversified Multi-Beta Multi-Strategy (Sector Neutral) 6-Factor 4-Strategy EW Market Beta Adjusted (Overlay) index to 1.01% for the SciBeta United States High-Factor-Intensity Diversified Multi-Beta Multi-Strategy 6-Factor 4-Strategy EW index.

In 2018, notably in the Developed, Developed ex-USA and United States regions, the poor performance of the Value and High Momentum smart factor indices in particular had a negative effect on relative performance, and sector neutrality did not significantly improve returns as was the case in 2017 when sector-based arbitrage, notably the tech rally, had a much greater impact on the performance of factor strategies which are "naturally" differently exposed to the sector than the broad cap-weighted indices. Nevertheless, the sector neutral versions remain the best performing indices among the Scientific Beta indices, with regional differences being observed. Last year, the SciBeta Extended Developed Europe High-Factor-Intensity Diversified Multi-Beta Multi-Strategy (Sector Neutral) 6-Factor 4-Strategy EW index posted a relative return of 2.92%, while the SciBeta Developed Asia-Pacific ex-Japan High-Factor-Intensity Diversified Multi-Beta Multi-Strategy (Sector Neutral) 6-Factor 4-Strategy EW index posted a relative return of -3.37%.

Over the past ten years, the SciBeta Developed Multi-Beta Multi-Strategy Four-Factor EW index, the SciBeta Developed High-Factor-Intensity Diversified Multi-Beta Multi-Strategy 6-Factor 4-Strategy EW index, the SciBeta Developed High-Factor-Intensity Diversified Multi-Beta Multi-Strategy (Sector Neutral) 6-Factor 4-Strategy EW index, the SciBeta Developed High-Factor-Intensity Diversified Multi-Beta Multi-Strategy 6-Factor 4-Strategy EW Market Beta Adjusted (Overlay) index and the SciBeta Developed High-Factor-Intensity Diversified Multi-Beta Multi-Strategy (Sector Neutral) 6-Factor 4-Strategy EW Market Beta Adjusted (Overlay) index posted strong annual relative returns of 1.10%, 2.19%, 1.93%, 3.75% and 3.14% respectively, compared to cap-weighted indices. For the other regions in the above table, the highest performance over the past ten years was obtained by the SciBeta Global High-Factor-Intensity Diversified Multi-Beta Multi-Strategy 6-Factor 4-Strategy EW Market Beta Adjusted (Overlay) index with a relative return of 3.83%, compared to cap-weighted indices, with the lowest performance posted by the SciBeta United States Multi-Beta Multi-Strategy Four-Factor EW index at 0.39%.

Over the long-term, all Scientific Beta Multi-Beta Multi-Strategy indices post positive excess returns compared to cap-weighted indices. Using long-term US track records from December 31, 1977 to December 31, 2017 (40 years), the SciBeta High-Factor-Intensity Diversified Multi-Beta Multi-Strategy Four-Factor Four-Strategy EW index, the SciBeta High-Factor-Intensity Diversified Multi-Beta Multi-Strategy 6-Factor 4-Strategy EW index, its Sector Neutral, Market Beta Adjusted (Overlay) and combined Sector Neutral/Market Beta Adjusted (Overlay) versions post respective relative returns compared to cap-weighted indices of 2.84%, 2.88%, 2.56%, 3.84% and 3.29%.

The relative and absolute performance data for Scientific Beta regional universes is available here.

Live Performance

The table below reports the live performance of the Scientific Beta Multi-Beta Multi-Strategy indices for the Developed ex-USA and United States regions as of 31 December, 2018.

Region	Multi-Beta Multi-Strategy Index	Nº Constituents	Annualised Relative Return Compared to Cap-Weighted	Annualised Absolute Return	Volatility	Sharpe Ratio
			From 20/12/2013 to 31/12/2018
Developed ex-USA	4-Factor EW	1154	2.07%	3.61%	11.09%	0.27
	HFI 6-Factor 4-Strategy EW	792	3.34%	4.88%	11.09%	0.38
	HFI 6-Factor 4-Strategy EW Sector Neutral	803	3.29%	4.83%	11.04%	0.38
	HFI 6-Factor 4-Strategy EW Market Beta Adjusted (Overlay)	792	3.55%	5.09%	12.27%	0.36
	HFI 6-Factor 4-Strategy EW Sector Neutral Market Beta Adjusted (Overlay)	803	3.48%	5.02%	12.25%	0.36
United States	4-Factor EW	462	-0.53%	8.16%	12.11%	0.62
	HFI 6-Factor 4-Strategy EW	316	0.53%	9.21%	11.84%	0.73
	HFI 6-Factor 4-Strategy EW Sector Neutral	318	0.73%	9.41%	12.42%	0.71
	HFI 6-Factor 4-Strategy EW Market Beta Adjusted (Overlay)	316	1.59%	10.28%	13.34%	0.72
	HFI 6-Factor 4-Strategy EW Sector Neutral Market Beta Adjusted (Overlay)	318	1.46%	10.14%	13.55%	0.70

Based on daily total returns in USD. The live date of the first generation Multi-Beta Multi-Strategy 4-Factor EW indices, i.e. 20/12/2013, is used as the basis as all the multi-factor indices proposed by Scientific Beta are derived from the same strategy. Although the other indices were created more recently, the longest live period is used for comparison purposes. The statistics are annualised and performance ratios that involve the average returns are based on the geometric average, which reliably reflects multiple holding period returns for investors. The risk-free rates used are defined according to the regional universe of the index.

Over their live period, all but one of the above Scientific Beta Multi-Beta Multi-Strategy indices posted positive relative returns compared to the cap-weighted benchmark.

The live performance data for Scientific Beta regional universes is available here.

Quarterly Smart Beta Performance Report, December 2018

White Papers

In a concern for transparency, and as part of its aim to help investors to understand and to invest in smart beta equity strategies, Scientific Beta has published a large number of white papers that are freely available on the Scientific Beta platform.

Highlighted White Papers

Measuring Factor Exposure Better to Manage Factor Allocation Better: A Critical Approach to Popular Factor Box Initiatives
October 2018

This paper presents two contributions on the subject of factor measurement. In the first contribution, entitled "Exposed to Nonsense? Spurious Factors in Popular Investment Tools," the authors question the choice of factor menu and factor proxies that are supposed to represent, and above all to standardise, the measurement of a portfolio's factor exposure. Their conclusion is indisputable: the factor definitions used by popular tools offered to investors are not supported by serious academic research. The second contribution, entitled "Mismeasurement of Factor Exposures in Score-Based Analytics Tools," questions the way in which the measurement of factor proxies is implemented. Here again, the observations should tell investors something.

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Investability of Scientific Beta Indices
December 2018

With the advent of smart beta equity indices, which represent alternatives to market-cap weighted indices, a major question on their investability has also been raised: at what cost will investors be able to trade the index constituents in the same proportions as the underlying strategy? The objective of this paper is to describe how Scientific Beta ensures the investability of its indices with the use of turnover controls and liquidity constraints, as well as to present the resulting turnover and liquidity measures of these very indices.

All White Papers

• A Guide to Scientific Beta Multi-Smart Factor Indices
• Adding Value with Factor Indices: Sound Design Choices and Explicit Risk-Control Options Matter
• Measuring Factor Exposure Better to Manage Factor Allocation Better: A Critical Approach to Popular Factor Box Initiatives
• Misconceptions and Mis-selling in Smart Beta: Improving the Risk Conversation in the Smart Beta Space
• Scientific Beta Multi-Beta Multi-Strategy Solution Benchmarks
• Tackling the Market Beta Gap: Taking Market Beta Risk into Account in Long-Only Multi-Factor Strategies
• How to Harvest Factor Premia without Suffering from Market Volatility: The Case for a Long/Short Multi-Factor Strategy
• Accounting for Cross-Factor Interactions in Multi-Factor Portfolios: The Case for Multi-Beta Multi-Strategy High Factor Exposure Indices
• Why we do not Believe that Maximising Factor Intensity at Stock Level is a Robust Approach to Multi-Factor Investing
• Academic Lessons on Factor Investing
• The Link between Factor Investing and Carbon Emissions
• Robustness of Smart Beta Strategies
• Investability of Scientific Beta Indices
• Scientific Beta Analytics: Examining the Performance and Risks of Smart Beta Strategies
• Overview of Diversification Strategy Indices
• Scientific Beta Maximum Deconcentration Indices
• Scientific Beta Maximum Decorrelation Indices
• Scientific Beta Efficient Minimum Volatility Indices
• Scientific Beta Efficient Maximum Sharpe Ratio Indices
• Scientific Beta Diversified Risk Weighted Indices
• Scientific Beta Diversified Multi-Strategy Index
• Scientific Beta Global Universe

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Supplements in Partnership with Industry Publications

EDHEC has established partnerships with a number of industry publications to produce special editorial supplements providing industry-relevant research of the highest academic standards.

P&I Research for Institutional Money Management
December 2018

The latest Scientific Beta special issue of the Research for Institutional Money Management supplement to P&I contains a number of articles on the subject of smart beta.

Adding Value with Factor Indexes
We show that achieving robust exposure to long-term rewarded factors, good diversification of unrewarded risks, and high levels of investability are key requirements for adding value with factor indexes. It is clear that this added value is expressed over the long term, but that risk control options can increase the short-term consistency of the outperformance that investors expect.

The Management of Sector Risk in Factor Investing
In a short-term risk control context, we review the sector-risk-control option offered to investors by Scientific Beta that has been available on its platform since its launch in 2013. We conclude that the choice of using the sector risk control option is a trade-off between investors' aversion to short-term risks generated by sector risk and their willingness to harvest factor risk premia in the most efficient way, to achieve the highest risk-adjusted performance over the long run.

Handling Macroeconomic Risks in Smart Beta Portfolios
On the subject of allocation between factors, Scientific Beta has undertaken extensive research to go beyond the usual approaches based on factor deconcentration or factor balance. In particular, we focus our research on the link between economic states and factor risk premia. We review some of the existing studies in this area from practitioners and then discuss conceptual considerations regarding the selection of relevant variables and propose a methodology for classifying macroeconomic regimes. We analyse the conditionality of factor premia to the macro regimes and give illustrative examples for implications for factor investors.

Factor Investing from a Total Portfolio Construction Perspective
In this article, the portfolio construction team at OPTrust explain their approach to factor investing from a total portfolio construction perspective. They believe that their mission is best accomplished by building a portfolio with balanced exposures across different risk factors, including macro and style factors. Constructing the portfolio in this way has reduced their dependence on common risk drivers, such as equity risk, to earn the returns they need to keep their plan sustainable, at the lowest level of risk.

Spurious Factors in Popular Investment Tools
Since allocating to factors implies that one knows how to identify them and how to measure a portfolio's exposures to them, we examine factor definitions used in analytic tools offered to investors and contrast them with the standard academic factors. We also outline why the methodologies used in popular tools pose a high risk of ending up with irrelevant factors.

Mismeasurement of Factor Exposures in Score-Based Analytics Tools
Most popular factor analysis tools used by investors deviate from the models used in research because they choose to use factor scores instead of betas. Although scores are easy to compute and present a point-in-time snapshot of portfolio characteristics, factor scores have serious shortcomings when it comes to factor exposure measurement. The consequence of this misalignment is that investors may end up with returns that fall short of their expectations.

Scientific Beta Named "Indexing Firm of the Year 2019" at the Annual Risk Awards

Scientific Beta is pleased to announce that it has won the prestigious Risk Award for "Indexing Firm of the Year 2019" at the annual awards ceremony held at the Brewery in London on November 27. The Risk Awards are the longest-running and most prestigious awards for firms and individuals involved in the global derivatives markets and in risk management. Scientific Beta succeeds Bloomberg, the 2018 award winner.

Accepting the prize, Scientific Beta CEO, Professor Noël Amenc, said, "It is particularly pleasing for Scientific Beta to be recognised for its work in the area of risk management for smart beta indices, because this is something that we have always prioritised in the development of our indices. Our view is that investors have a fiduciary responsibility to manage risk and our role and duty is to provide them with appropriate risk diversification and non-factor risk-control mechanisms such as our market-beta-adjustment and sector-neutral options. We think that the search for smart beta outperformance should not sacrifice the sound risk management of such strategies and it is probably this sound risk management that allows an index or a factor or alternative weighting strategy to be qualified as 'smart'."

Scientific Beta launched six years ago with the missions of maximising the impact of EDHEC Business School's academic research in the indexing space. The organisation started with 12 people; now it has 52 and is hiring more. Scientific Beta's assets under replication were USD 43bn at the end of December 2018.

This business impact of an academic venture has no equivalent. EDHEC Business School, whose finance courses are regularly ranked among the best in the world in the international rankings, has been able to mobilise researchers to go beyond education and allow fast adoption of the results of its research in the area of smart beta and factor investing.

More on the Risk Award

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Scientific Beta ESG Filter Allowed Renault, Nissan and Mitsubishi to be Excluded from ESG Indices

A pioneer in the reconciliation of non-financial objectives and financial performance through responsible investment smart beta, Scientific Beta administers responsible investment indices that are tracked by circa USD15bn of institutional money. Scientific Beta was recently selected by Desjardins Global Asset Management to design a family of indices that respect high responsible investment standards and materially reduce exposure to climate change risk.

While encouraging better corporate practices in environmental, social and governance (ESG) matters, the indices in the SciBeta Desjardins Responsible Investing family also exclude companies that are involved in controversial activities or in critical controversies with respect to principles of responsible business conduct. Critical controversies concern fundamental issues and have high adverse impact on a large scope; fundamental issues are defined in reference to the Ten Principles of the United Nations Global Compact, which cover human rights, labour, environment and anti-corruption and are derived from international conventions and declarations that enjoy global support.

"Being involved in critical controversies in the area of the environment (Renault is suspected of having cheated on emissions tests, Nissan has admitted to it and Mitsubishi has been fined for misrepresenting the fuel efficiency of its vehicles), none of these companies was eligible for inclusion in the SciBeta Desjardins Responsible Investing indices at the latest rebalance," Frederic Ducoulombier, Director of Risk and Compliance at Scientific Beta, explains.

By separating ESG performance and financial performance objectives in index construction, Scientific Beta ESG indices treat responsible investing policies as fiduciary constraints and build smart beta indices that can add performance in a robust manner by establishing exposure to rewarded risk factors once these exclusions have been carried out. This means that unacceptable ESG performance cannot be offset by attractive financial characteristics and reciprocally, that the ESG characteristics of compliant stocks are not allowed to water down factor tilting or oppose scientific diversification.

This clearly differentiates Scientific Beta's construction approach from investment strategies that select or weight securities on the basis of metrics integrating ESG and financial data and are often promoted by advocates of the integrated ESG approach.

For Noël Amenc, CEO of Scientific Beta, "traditional integrated approaches promoted by a large majority of ESG solution providers that target an ESG or low carbon score without any exclusions and ultimately offset the poor ESG scores of some companies with the good ESG scores of other companies or, worse, offset the poor ESG score of a firm with the same firm’s good exposures to selected financial factors, are practices that do not correspond to what a truly responsible investor should implement."

"In a passive investment context, outright exclusions on the basis of international norms and relative ESG performance also send clear signals to companies regarding the progress they need to make in respect of environmental, social and governance issues to meet their basic responsibilities and position for sustainable success," adds Noël Amenc.

ESG Incorporation – A Review of Scientific Beta’s Philosophy and Capabilities, Scientific Beta White Paper, September 2018

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Assets Replicating Scientific Beta's Multi-Factor Indices Reach USD 43bn

Scientific Beta has announced that assets tracking its smart beta indices reached USD 43bn at December 31, 2018. Compared to December 31, 2017, this amount of assets under replication represents an increase of USD 18bn, corresponding to one-year growth of 72%.

This growth comes from the success of Scientific Beta's multi-factor offerings. The Scientific Beta Multi-Beta Multi-Strategy Four-Factor EW indices, which were the first multi-factor indices to be offered by Scientific Beta, show an average live annualised outperformance across all Scientific Beta Developed regions of 1.49% over their five-year live track record and an improvement in Sharpe Ratio of 52.36% compared to their cap-weighted benchmark¹.

Noël Amenc, CEO of Scientific Beta, said, "We have been very pleased with the continued growth of our assets under replication in conditions that were not necessarily favourable for some factor strategies. We are continuing to highlight the importance for our clients of risk management in the factor space.

Smart beta strategies are selected to provide explicit exposure to some well-rewarded factors (Value, Momentum, Low Volatility, Profitability, Low Investment, Size). These factors provide good risk-adjusted returns over the long-term but they are also exposed to a number of hidden or implicit risks that drive short-term performance, like for example sector risk or even a market beta bias. These risks can therefore cause big disappointments for investors if they are not understood and possibly managed. These issues have been underlined in a recent Scientific Beta publication entitled Misconceptions and Mis-selling in Smart Beta: Improving the Risk Conversation in the Smart Beta Space. One of our success factors is to analyse these risks, make them explicit and allow investors to hedge them or not depending on their fiduciary objectives and constraints."

Misconceptions and Mis-selling in Smart Beta: Improving the Risk Conversation in the Smart Beta Space, Scientific Beta White Paper, February 2018

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¹The average live outperformance and improvement in Sharpe Ratio across all Scientific Beta developed regions of Scientific Beta Multi-Beta Multi-Strategy Equal-Weight indices is 1.49% for the outperformance and 52.36% for the improvement in Sharpe Ratio. This live analysis is based on daily total returns in the period from December 20, 2013 (live date) to December 31, 2018 for all diversified multi-strategy indices that have more than 3 years of track record for all available developed world regions - USA, Eurozone, UK, Developed Europe, Developed Europe ex UK, Japan, Developed Asia Pacific ex Japan, Developed ex UK, Developed ex USA and Developed. The benchmark used is a cap-weighted portfolio of all stocks in the respective Scientific Beta universes.

EDHEC Scientific Beta Days 2019 European and North American Conferences – Save the Dates

10-11 October, 2019 – The Barbizon Palace, Amsterdam, The Netherlands
24-25 October, 2019 – The Ritz Carlton, Boston, United States

These annual two-day conferences, organised by Scientific Beta in Europe and North America, will present the asset owner and financial advisory communities with the latest conceptual advances and research results in smart beta investing, enabling their implications and applications to be discussed with researchers who combine expertise of advanced financial techniques with a sound awareness of their industry relevance. The events will include multiple plenary sessions, workshops and practical sessions allowing professionals to review major industry challenges, explore state-of-the-art investment techniques and benchmark practices to advances in research.

This year, the conferences will focus on the following themes:

• Is it smart and responsible to invest in cryptocurrencies?
• Can factor risk premiums disappear with their success?
• Is there an ESG risk premium?
• How to reconcile ESG and factor investing
• Limiting the carbon impact of factor strategies
• How to manage the risks of factor-based investment
• Protecting equity investment against drawdown risk: the case for maximum drawdown and maximum loss risk control options
• The importance of market beta neutrality in the performance of factor investing in long-only and long-short
• Modifying the factor exposures of an existing portfolio: factor completeness portfolio and single factor allocation
• What factor allocation to reduce the dependence of the performance of multi-factor strategies on economic regimes? From factor balance to regime premia diversification
• How to assess the robustness of a multi-factor strategy
• Are some defensive strategies more efficient than others?

The detailed programmes will be available shortly.

The conferences are reserved for asset owners (including pension schemes, charities, endowments, foundations, insurance companies, single family offices and financial executives from non-financial companies) and institutional consultants.

Admission is complimentary and by invitation only. Registrations will open in March 2019.

For further information about these events, please contact Joanne Finlay at scientificbetadays@scientificbeta.com.

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EDHEC Smart Beta Day Germany 2019 Conference

4 June, 2019 – Frankfurt, Germany

The conference, organised by Scientific Beta, will enable asset owners and their direct investment consultants and financial advisors to access the latest conceptual advances and research results in smart beta investing and to discuss their implications and applications with researchers who combine expertise of advanced financial techniques with a sound awareness of their industry relevance. A number of presentations at the event will be held in German.

Multiple plenary sessions, allowing professionals to review major industry challenges, explore state-of-the-art investment techniques and benchmark practices to advances in research, will address the following themes:

• Are the benefits of factor investing victims of their success?
  • Impact of factor investing success on the market and the impact on transaction costs.
  • What are the performance drivers of multi-factor investing over the last three years?
  • Why the performance drivers of factors?
  
  
• Taking non-factor risks into account in the construction of multi-factor strategies
  • The importance of good diversification of specific risks to efficiently capture factor risk premia.
  • Taking into account systematic (market and sector) non-factor risks in multi-factor construction.
  • What impact do multi-factor portfolio construction choices have on their performance and risk profiles?

• Can we rely on scores to measure factor exposures?
  • The limitations of score-based approaches to measure factor exposure.
  • What are the alternatives to score-based approaches?
  • Practical case: Scientific Analytics: Analysing a multi-strategy multi-manager core-portfolio based on returns alone.

• Limitations of integrated ESG approaches
  • Principles of integrated ESG on factor investing.
  • Limits of integrated ESG on factor investing.
  • Taking into account a strict ESG policy as part of a factor investing approach.
  
  
• What are the macroeconomic risks of factor strategies?
  • How realistic are the interest rate risks incorporated in factor strategies?
  • How do we measure exposure to macro-factors in the equity space?
  • How do we take these exposures into account in the portfolio construction process?
  
  
• Practical case: How do we modify the factor exposure of a core portfolio?
  • Completeness portfolio strategy versus overlay portfolio strategy.
  • Long-only factors versus long/short factor completeness ingredients.
  • Practical case: Implementing a factor strategy using traditional active managers.

The conference is reserved for asset owners (including pension schemes, charities, endowments, foundations, insurance companies, single family offices and financial executives from non-financial companies) and institutional consultants.

Admission is complimentary and by invitation only. To request an invitation to the conference, please visit the dedicated registration website.

For further information about this event, please contact Séverine Cibelly at severine.cibelly@scientificbeta.com.

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IPE Webcast: "Exposed to Nonsense? Spurious Factors in Popular Investment Tools"

21 March, 2019 – 3.00pm GMT

Felix Goltz, Research Director, Scientific Beta, and Head of Applied Research at EDHEC-Risk Institute, will be hosting an IPE webcast entitled "Exposed to Nonsense? Spurious Factors in Popular Investment Tools" on Thursday 21 March, 2019 at 3.00pm GMT.

Factor investing offers a big promise. By identifying the persistent drivers of long-term returns in their portfolios, investors can understand which risks they are exposed to, and make explicit choices about these exposures.

When it comes to information about factors, providers offer analytic toolkits to identify the factor exposures of an investor's portfolio. However, these analytic tools do not employ academically grounded factors and their factor finding process maximises the risk of ending up with false factors. These non-standard factors also lead to mismeasurement of exposures and may capture exposure to redundant factors. In the end, analytic tools for investors do not deliver on the promise of factor investing and they also lack transparency.

This webinar, moderated by Brendan Maton from IPE, will contrast factor definitions used in analytic tools offered to investors and compare them with the standard academic factors. We will also outline why the methodologies used in popular tools pose a high risk of ending up with irrelevant factors.

To register for the webcast, please visit the dedicated webpage.

 

As part of its international development programme and in order to strengthen its index development activity, the EDHEC group, one of Europe's leading research and teaching institutions, is recruiting for positions within Scientific Beta in London, Singapore and Nice. To apply, please send your CV and a cover letter to recruitment@scientificbeta.com.

• Salaries are determined according to the EDHEC pay scale, based on qualifications and prior experience.
• Written and spoken English is essential, and basic spoken French is a plus.
• An EU work permit is mandatory for the positions in Scientific Beta's Nice and London offices and a US work permit is mandatory for the position in Boston.

For more information about Scientific Beta, please visit our website and our corporate YouTube channel.

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Investment Solutions and Product Specialist, Scientific Beta (London or Boston)

The successful candidate will be responsible for providing support to clients and users on conceptual frameworks, features and calculations for single and multi-factor indices. This will require liaising with technical and research teams to meet client demands and responding to highly analytical/quantitative inquiries. The role also involves supporting the Business Development Manager on sales activities with expert presentations to clients and providing advice on the implementation of multi-factor solutions for clients on the basis of the wide range of indices designed by Scientific Beta in coordination with the research teams.

The successful candidate should have a quantitative educational background (statistics, mathematics, finance). A PhD would be a plus. Prior client-facing experience within a financial institution, preferably in portfolio management, is necessary, together with financial markets skills, extensive knowledge of asset pricing and multi-factor products. The candidate should also be able to communicate complex quantitative academic literature to audiences of asset owners and to work independently in a small team, showing initiative to support clients and prospects with the goal of winning business together with the Business Development Managers.

This is a global position that is based in London or Boston but requires frequent travel to Europe and North America.

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Senior Quantitative Analyst, Scientific Beta (Nice or London)

The successful candidate will be a senior quantitative analyst with significant skills in quantitative equity portfolio construction and equity factor investing implementation. A minimum of 10 years' experience is necessary in the field. The position requires a Master's degree in Finance or Financial Engineering from a leading institution and experience in constructing quantitative equity portfolios within an equity index provider, investment bank, or asset management firm.

The candidate will support the Client Services team in analysing and coordinating client requests (index simulation, index performance analysis, client reports). Experience in drafting and publishing equity analysis reports for a broad audience in English is a must. The candidate also needs to have a sound command of Matlab for financial computations, in particular in the area of portfolio construction and performance analysis.

Flexibility, responsiveness and team spirit are essential.

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Quantitative Equity Analyst, Scientific Beta (Singapore)

The successful candidate will contribute to Scientific Beta's product development in equity factor investing by providing development and validation of systematic equity investment strategies and statistical tools for performance and risk analysis, notably through the validation and replication of systematic equity strategies through independent backtests, the development and validation of statistical tools to assess the robustness of equity factor strategies and the production of technical documentation of statistical tools, investment strategies and validation procedures.

The successful candidate will be a quantitative equity analyst with significant skills in the backtesting of systematic equity investment strategies and the development of statistical tools to assess performance and risks. The position requires a Master's degree in Finance, Financial Engineering or a related discipline from a leading institution and experience of several years in quantitative analysis obtained within a provider of analytic tools, an investment bank, or an asset management firm. The candidate needs to have strong knowledge of Matlab for financial computations, particularly in the area of portfolio construction and statistical analysis. Other programming skills would be a plus. Capacity to document work processes and communicate effectively with different stakeholders is necessary. Experience in equity index calculation and working with equity index constituent data would be a plus.

Scientific Beta

Scientific Beta aims to be the first provider of a smart beta indices platform to help investors understand and invest in advanced beta equity strategies.

Established by EDHEC-Risk Institute, one of the very top academic institutions in the field of fundamental and applied research for the investment industry, Scientific Beta shares the same concern for scientific rigour and veracity, which it applies to all the services that it offers investors and asset managers.

The Scientific Beta offering covers three major services:

• Scientific Beta Indices are smart beta indices that aim to be the reference for the investment and analysis of alternative beta strategies. Scientific Beta Indices reflect the state-of-the-art in the construction of different alternative beta strategies and allow for a flexible choice among a wide range of options at each stage of their construction process. This choice enables users of the platform to construct their own benchmark, thus controlling the risks of investing in this new type of beta (Smart Beta 2.0).
  
  Within the framework of Smart Beta 2.0 offerings, Scientific Beta provides access to smart factor indices, which give exposure to risk factors that are well rewarded over the long term while at the same time diversifying away unrewarded specific risks. By combining these smart factor indices, one can design very high performance passive investment solutions.
  
  
• Scientific Beta Analytics are detailed analytics and exhaustive information on its smart beta indices to allow investors to evaluate the advanced beta strategies in terms of risk and performance. The analytics capabilities include risk and performance assessments, factor and sector attribution, and relative risk assessment. Scientific Beta Analytics also allow the liquidity, turnover and diversification quality of the indices offered to be analysed. In the same way, analytics provide an evaluation of the probability of out-of-sample outperformance of the various strategies present on the platform.
  
  
• Scientific Beta Fully-Customised Benchmarks and Smart Beta Solutions is a service proposed by Scientific Beta, and its partners, in the context of an advisory relationship for the construction and implementation of benchmarks specially designed to meet the specific objectives and constraints of investors and asset managers. This service notably offers the possibility of determining specific combinations of factors, considering optimal combinations of smart beta strategies, defining a stock universe specific to the investor, and taking account of specific risk constraints during the benchmark construction process.

With a concern to provide worldwide client servicing, Scientific Beta is present in Boston, London, Nice, Singapore and Tokyo. As of December 31, 2018, there was USD 43bn in assets replicating Scientific Beta indices. 42% of these assets under replication are ESG-compliant. Scientific Beta has a dedicated team of 52 people who cover not only client support from Nice, Singapore and Boston, but also the development, production and promotion of its index offering. Scientific Beta signed the United Nations-supported Principles for Responsible Investment (PRI) on September 27, 2016.

Scientific Beta
1 George Street, #15-02, Singapore 049145
Tel. +33 493 187 851 (from 9.00am to 6.00pm CET)
E-mail: clientservices@scientificbeta.com | Website: www.scientificbeta.com

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