Out-of-sample Dataset Before the “Sample”: Pervasive Anomalies Before 1926

30.November 2021

Data are the key to systematic investing/trading strategies. The hypotheses testing, risk or return evaluations, correlations, and factor loadings rely on past data and backtests. With an increasing speed of publication in finance, critiques of quantitative strategies have emerged. Strategies seem to decay in alpha, post-publication returns tend to be lower, and many strategies become insignificant once rigorously tested (in or out-of-sample). Moreover, some might even appear profitable purely by chance and the repetitive examination of the same dataset, such as CRSP stocks after 1963. 

Is there any solution to overcome these limitations? Partially, the design of the novel machine learning strategies consisting of training, validation, and testing sets might help. Perhaps the most crucial part of such a scheme is the usage of the purely out-of-sample dataset. In this regard, the novel research by Baltussen et al. (2021) provides several valuable findings for the most recognized factors. The authors constructed a database of U.S. stocks, including dividends and market caps for 1488 major stocks from 1866 to 1926. The sample can be described as the pre-CRSP period, including independent, pre-publication, and “out-of-sample” data that can be a perfect test for the factors utilized today. 

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Community Alpha of QuantConnect – Part 2: Social Trading Factor Strategies

13.August 2021

This blog post is the continuation of series about Quantconnect’s Alpha market strategies. Part 1 can be found here. This part is related to the factor strategies notoriously known from the majority of asset classes.

Overall, the factors on alpha strategies provide insightful results that could be utilized. The results particularly point to excluding the most extreme strategies based on various past distribution’s characteristics.

Stay tuned for the 3rd and 4th part of this series, where we will explore factor meta-strategies built on top of the QuantConnect’s Alpha Market.

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An Important Analysis of Stock Momentum and Reversal Factors

11.August 2021

Can we explain stock momentum by industry, sector or factor momentum? Moreover, a similar question could be raised about the short-term reversal. The novel research by Li and Turkington (2021) uses a robust regression model to divide momentum and reversal returns into the main drivers. The individual momentum anomaly that broader market groups do not fully explain exists in the whole sample but is statistically weak. On the other hand, the reversal anomaly is highly significant. Secondly, the traditional 12-months momentum can be better explained by the factor momentum than the industry or sector momentum. Still, the industries, industry groups, sectors, and even factors have distinct drivers, and the anomalies seem different.

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Trading Index (TRIN) – Formula, Calculation & Trading Strategy in Python

14.December 2020

Short-term mean reversion trading on equity indexes is a popular trading style. Often, price-based technical indicators like RSI, CCI are used to assess if the stock market is in overbought or oversold conditions. A new research article written by Chainika Thakar and Rekhit Pachanekar explores a different indicator – TRIN, which compares the number of advancing and declining stocks to the advancing and declining volume. TRIN’s advantage is that it’s cross-sectionally based and its calculation uses not only price but also volume information. Thakar& Pachanekar’s research paper is useful for fans of indicator’s based trading strategies and offers a short introduction to TRIN’s calculation together with an example of mean-reversion market timing strategy written in a python code.

Authors: Chainika Thakar, Rekhit Pachanekar

Title: Trading Index (TRIN) – Formula, Calculation & Strategy in Python

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Stock Price Overreaction to ESG Controversies

23.November 2020

Nobody can doubt that in the recent period, ESG investing has significantly grown and is a staple part of the financial markets. The academic literature has also grown with the popularity of ESG investing. The negative, mixed and positive results for ESG scores in portfolios have evolved, and generally, there is a consent that ESG scoring can be a vital part of the portfolio management process. It can be observed that in the past, the ESG scores were not priced in the equity market and still, the ESG is not priced in the corporate bond market (apart from Europe). Nowadays, the investors react to the ESG scores, but the research paper of Cui and Docherty (2020) has novel insights that investors may react too much to the ESG. Their research shows that investors overreact to the negative ESG events and stocks connected with negative ESG events sharply fall, but the prices have mean-reverting properties. As a result, there is a reversal after bad ESG events. Stocks firstly sharply fall, but then their prices are reverted to the previous values. Therefore, this paper is interesting from the market pricing or efficiency point, but it also can be utilized by a reversal investor.

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