AlphaBetaWorks

Investment Risk Management, Skill Evaluation, and Predictive Performance Analytics

[AlphaBetaWorks @AlphaBetaWorks]

#Quants and #AltData producers with great results endure skepticism and long trial periods. The following article explains why even audited returns do not address the concerns of allocators and data buyers.

[Greg Kapoustin @GregKapoustin]

#Quant investors and #AlternativeData producers with strong results are often puzzled by long evaluation periods. Here’s why great performance, even when audited, does not address survivorship bias, Sybil attacks, and hidden risks: https://t.co/7i89o82RDM

[AlphaBetaWorks @AlphaBetaWorks]

The #patent “lottery” in which applications receive lenient examiners reveals the value of patents for #startups. On average, winning the lottery leads to 55% higher employment growth and 80% higher sales growth five years later: https://t.co/Y7avFX6KBJ

[AlphaBetaWorks @AlphaBetaWorks]

Tax-loss harvesting has been effective over nearly a century. Though #TaxAlpha has declined in recent decades as these techniques proliferated, it still has the potential to boost taxable portfolio returns by 0.5-1%: https://t.co/cqkbXoTSdu

[AlphaBetaWorks @AlphaBetaWorks]

In the natural language processing (#NLP) analysis of company conference calls, changes in tone and increases in negativity are particularly significant predictors of future earnings and uncertainty: https://t.co/0EqqEsda0z

[AlphaBetaWorks @AlphaBetaWorks]

Separating passive (long) and active (market-neutral) components of active investment can reduce tax costs: https://t.co/8KHm4uYRPR

[AlphaBetaWorks @AlphaBetaWorks]

Not all environmental, social, and governance (ESG) metrics are equally relevant to a firm’s value. Focusing on the material #ESG metrics can upgrade #esginvesting strategies: https://t.co/PAcwviUjk5

[AlphaBetaWorks @AlphaBetaWorks]

Investment strategies based on the #NaturalLanguageProcessing (#NLP) of financial news are fashionable. Yet, they tend to struggle as little news-related volatility is exploitable during the trading hours: https://t.co/kDT1K6A7yo

[AlphaBetaWorks @AlphaBetaWorks]

Naïve #currency #hedging of international stock portfolios is sub-optimal – intelligent optimization of currency exposures reduces risk or improves returns: https://t.co/8p4FxDOtjv

[AlphaBetaWorks @AlphaBetaWorks]

Global fixed income markets offer parallels to the equity #SmartBeta strategies as well as unique #factors. Specifically, the global curve carry delivers returns unexplainable by other factors and subsumes the bet-against-beta anomaly: https://t.co/QOpXuejcGo

[AlphaBetaWorks @AlphaBetaWorks]

Crowding does not affect all #SmartBeta strategies equally. Those based on the divergence of security properties, such as #momentum and quality, are much more vulnerable to crowding than those based on convergence, such as value: https://t.co/JA1EfWMKzO

[Tom Tully @tomsendeavour]

Great illustration of the explanatory power of #sectors vs style: "Whereas sector factors explain over 25% of the variance in market residuals for hundreds of stocks, style factors do so for only a small handful". @AlphaBetaWorks https://t.co/qbDGYccygZ #equityrisk

[AlphaBetaWorks @AlphaBetaWorks]

@benjaminMlavine @tpsarofagis Excellent observation. We also find that sector exposures along with market beta tend to explain stock returns, including style, with little need for separate style factors: https://t.co/ZJloBNo5ue, https://t.co/cgABhfxQI4

[AlphaBetaWorks @AlphaBetaWorks]

When evaluating a seductively high-performing smart beta or other quantitative investment strategy, one can wait a few years to see how things turn our, or one can stress it with the attention to the bias-variance tradeoff: https://t.co/ElyThhLyiU, https://t.co/AmEirsoHTV

[AlphaBetaWorks @AlphaBetaWorks]

A recent paper (https://t.co/XkS0cQEnhH) elucidates the struggles of simplistic #SmartBeta portfolios: US Value #ETF and Mutual Fund assets already exceed the estimated $300B basic US Value capacity (https://t.co/6CzWNoPeya, https://t.co/nBroIAjRaW).

[AlphaBetaWorks @AlphaBetaWorks]

#SmartBeta returns are 26% lower out-of-sample and 58% lower post-publication. As investors harvest simplistic published #factors, it is not surprising that they are failing to perform – it is surprising that some performed as long as they did: https://t.co/jaF1YW4bP9

[Greg Kapoustin @GregKapoustin]

A practical illustration of systematic investment strategy and manager selection based on strong short-term nominal performance, and how this produces momentum and reversion anomalies in the financial markets:

[AlphaBetaWorks @AlphaBetaWorks]

When modeling #equityrisk, the explanatory power of sector #factors is slightly higher than that of size and approximately four times greater than that of the #value/growth: https://t.co/ZJloBNo5ue

[AlphaBetaWorks @AlphaBetaWorks]

A new inefficiency arises as #ETF flows grow and blindly trade security baskets: when a theme moves an ETF, stocks with no exposure to the theme can experience significant price changes and subsequent reversions back to fundamentals: https://t.co/yOZa6WvlvZ

[AlphaBetaWorks @AlphaBetaWorks]

#TaxOptimization has the potential to boost the returns of portfolio replication, factor tilt, and #SmartBeta strategies by 1-2%, depending on the portfolio parameters and constraints: https://t.co/medoUKtaYA

[AlphaBetaWorks @AlphaBetaWorks]

@alphaarchitect @lorenfox7 @ToddCFRA @nategeraci @ETFStore Yes. One gets the replicating dumb-factor portfolio from the exposures of a given #SmartBeta #ETF, portfolio, or screen. For instance, here's the Pure Value ETF (RPV) -- mostly a long Market, long Financials, and short Technology bet: