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How to Deal With Missing Financial Data

February 25, 2023

The problem of missing financial data is widespread yet often overlooked. An interesting insight into the structure of missing financial data provides a novel research paper by authors Bryzgalova et al. (2022). Firstly, examining the dataset of the 45 most popular characteristics in asset pricing, the authors found that missing data is frequent among almost any characteristic and affects all kinds of firms – small, large, young, mature, profitable, or in financial distress. The requirement of multiple characteristics simultaneously makes the problem even worse. Moreover, the data is not missing randomly; missing values clusters both cross-sectionally and over time. This may lead to a selection bias, making most famous ad-hoc approaches like the median invalid. Considering the abovementioned findings, the authors propose a novel imputation method based on Principal Component Analysis (PCA).

Size Factor vs. Monetary Policy Regime

January 25, 2023

We have brought attention to the importance of evaluating factors models in different market regimes, and now, we will take a closer look at the size factor. Size [SMB (small minus big)] factor is a popular investment choice for asset investigation by many portfolio managers worldwide. The Size earned prominence in Fama and French’s three and five-factor models, and enjoy the continued discussion about its place in today’s portfolio construction. But it’s crucially important for investors seeking to capture the Size premium to realize that it is dependent on the monetary policy being pursued by the Federal Reserve, as the monetary easing seems to induce a Size premium.

Reviewing Patent-to-Market Trading Strategies

November 16, 2022

The following article is a short distillation of the research paper Leveraging the Technical Competence of a Stock for the Purpose of Trading written by Rishabh Gupta. The author spent a summer internship at Quantpedia, investigating the Patent-to-Market (PTM) ratio developed by Jiaping Qiu, Kevin Tseng, and Chao Zhang. The PTM ratio uses public information about the number and dates of patents assigned to publicly listed companies, calculates an expected market value of patents, and tries to predict future stock performance.

Impact of Dataset Selection on the Performance of Trading Strategies

November 14, 2022

It would be great if the investment factors and trading strategies worked all around the world without change and under all circumstances. But, unfortunately, it doesn’t work like that. Some of the strategies are market-specific, as shown in this short analysis. The Chinese market has its own specifics, mainly higher representation of retail investors and lower efficiency. And it’s not alone; countless strategies work just in cryptocurrencies, selected futures, or some other derivatives markets. So, what’s the takeaway? Simple, it’s really important to understand that each anomaly is linked to the underlying dataset and market structure, and we need to account for it in our backtesting process.

The Role of Interest Rates in Factor Discovery

October 24, 2022

Over the past several decades, economists and quantitative scientists found a very large number of asset pricing anomalies and published numerous research papers about their findings, and this is known in the financial jargon as “factor zoo.” However, one strong underlying force might drive the performance of many of those anomalies. What’s that force? The level and trend in the interest rates, as in almost all parts of the developed world, there was a long-term steady decline in rates and inflation for nearly 40 years. We use the past tense as it seems that the situation changed at the beginning of this year…

Van Binsbergen, Jules H. and Ma, Liang and Schwert, Michael (Sep 2022) touched on this subject and made a careful examination of both past factor research and found that a significant part of published papers and developed models are sometimes unknowingly exposed to fitting to low or even zero interest rates.

How to Improve Post-Earnings Announcement Drift with NLP Analysis

October 11, 2022

Post–earnings-announcement drift (abbr. PEAD) is a well-researched phenomenon that describes the tendency for a stock’s cumulative abnormal returns to drift in the direction of an earnings surprise for some time (several weeks or even several months) following an earnings announcement. There have been many explanations for the existence of this phenomenon. One of the most widely accepted explanations for the effect is that investors under-react to the earnings announcements. Although we already addressed such an effect in some of our previous articles and strategies, we now present a handy method of improving the PEAD by using linguistic analysis of earnings call transcripts.

A Study on How Algorithmic Traders Earn Money

September 13, 2022

Our mission here at Quantpedia is to provide both retail and institutional investors with ideas for trading strategies that are easily understandable while based on and backed by quantitative academic research. Today, we present you with the results from a study that we came across. Although it’s not quantitative, but qualitative, it has really held our interest. The paper does not provide any images or figures; it is a study made from various types of surveys with answers from professionals concluded with an attention-grabbing summary table.

Best Performing Value Strategies – Part 1

May 23, 2022

Equity Value strategies have suffered hardly during years 2018, 2019 and also 2020. Due to the poor performance of Value during this period, many investors have abandoned the strategy, often expressing view that “Value strategy is not working anymore”. Nevertheless, equity Value strategies have managed a strong comeback recently, turning attention of investors and traders back to them. In our blog today, we will take a close look at many different equity Value strategies, their performance and how they behave.

How Often Should We Rebalance Equity Factor Portfolios?

May 10, 2022

Quantpedia has already covered a countless number of factor investing strategies and articles, from strategies in our Screener to multiple blog posts. Therefore, we can confidently say that we do like factor investing. However, there is always new research with a unique point of view. For example, we recently found a paper focused on the decay of the factor exposures of equity factor strategies. The study examines five factors: Value, Momentum, Quality, Investment, and Low Volatility, across 12 developed and emerging markets over a 20-year period. This research aims to find out how long it takes for a factor to decay after the portfolio is assembled. In other words, how often should the portfolio be rebalanced?

How Does Weighting Scheme Impact Systematic Equity Portfolios?

April 27, 2022

How often do you think about the weights of the assets in your portfolio? Do you weigh your assets equally, or do you prefer value-weighting? The researchers behind a recent research paper analyzed various weighting schemes and examined their effect on factor strategy return. They studied five weighting schemes that ignore prices: equal weighting, rank weighting, z-score weighting, inverse volatility weighting, and fundamental weighting, and three price-based weighting schemes: Rank x mcap (rank-times-mcap), Z-score x mcap (z-score-times-mcap), and Integrated core.

They found that schemes that are not based on price can inflate turnover and costs. However, the weighting schemes based on price are the most practical to target multiple premiums, provide robust risk control, and decrease turnover and expenses.