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Is There Any Hidden Information in Annual Reports’ Images?

March 29, 2022

Can the number or type of images in a firm’s annual report tell us anything about the firm? Or is it just a marketing strategy that doesn’t hold any further information? With the help of novel machine learning techniques, the authors Azi Ben-Rephael, Joshua Ronen, Tavy Ronen, and Mi Zhou study this problem in their paper “Do Images Provide Relevant Information to Investors? An Exploratory Study”. It seems that the proposed metrics help to forecast some of the firms’ fundamental ratios.

How to Use Deep Order Flow Imbalance

October 6, 2021

Order book information is crucial for traders, but it can be complex. With the numbers of stocks listed in stock exchanges, it is impossible to track all the available information for the human mind. Therefore, the order flows could be an interesting dataset for machine learning models. The novel research of Kolm, Turiel and Westray (2021) utilizes deep-learning for high-frequency return forecasts for 115 NASDAQ stocks based on order book information at the most granular level.

The paper has several key contributions. Firstly, it does not forecast one single return but rather a whole vector of returns – a term structure consisting of mid-price return forecasts at a specified horizon. The forecasted term structure provides essential information about the most optimal execution algorithms (or a trading strategy). According to the authors, forecasts have an „accuracy peak“ at two price changes, after which the accuracy declines. Secondly, the paper compares several methods: autoregressive model with exogenous inputs, MLP, LSTM, LSTM-MLP, stacked LSTM, and CNN-LSTM. Therefore, the article could also serve as a horse race across several possible forecasting methods. Lastly, using more traditional statistical approaches, the authors have identified a better forecasting performance in more information-rich stocks. As a result, this novel research could benefit many areas such as high-frequency trading (but trading costs must be considered), optimal execution strategy, or market-making.

Introduction to Clustering Methods In Portfolio Management – Part 1

September 16, 2021

At the beginning of October, we plan to introduce for our Quantpedia Pro clients a new Quantpedia Pro report dedicated to clustering methods in portfolio management. The theory behind this report is more extensive; therefore, we have decided to split the introduction into our methodology into three parts. We will publish them in the next few weeks before we officially unveil our reporting tool. This first short blog post introduces three clustering methods as well as three methods that select the optimal number of clusters. The second blog will apply all three methods to model ETF portfolios, and the final blog will show how to use portfolio clustering to build multi-asset trading strategies.

How to Use Lexical Density of Company Filings

September 10, 2021

The application of alternative data is currently a strong trend in the investment industry. We, too, analyzed few datasets in the past, be it ESG data, sentiment, or company fillings. This article continues the exploration of the alt-data space. This time, we use the research paper by Joenväärä et al., which shows that lexically diverse hedge funds outperform lexically homogeneous as an inspiration for us to analyze various lexical metrics in 10-K & 10-Q reports. Once again, we show that it makes sense to transmit ideas from one research paper to completely different asset class.

New Machine Learning Model for CEOs Facial Expressions

August 9, 2021

Nowadays, it is a standard that fillings such as 10-Ks and 10-Qs are analyzed with machine learning models. ML models can extract sentiment, similarity metrics and many more. However, words are not everything, and we humans also communicate in other forms. For example, we show our emotions through facial expressions, but the research on this topic in finance is scarce. Novel research by Banker et al. (2021) fills the gap and examines the CEOs facial expressions during CNBC’s video interviews about corporate earnings.

Man vs. Machine: Stock Analysis

July 17, 2021

Nowadays, we see an increasing number of machine learning based strategies and other related financial analyses. But can the machines replace us? Undoubtedly, AI algorithms have greater capacities to “digest” big data, but as always in the markets, everything is not rational. Cao et al. (2021) dives deeper into this topic and examines the stock analysts. Target prices and earnings forecasts are crucial parts of the investing practice and are frequently used by traders and investors (and even ML-based strategies). The novel research examines and compares the abilities of human analysts versus the AI algorithm in forecasting the target price. As a whole, AI-based analysts, on average, outperforms human analysts, but it is not that straightforward. While AI can learn from large datasets, humans do not seem to be replaced soon. There are certain fields where human uniqueness is valuable. For example, in illiquid and smaller firms or firms with asset-light business models. Moreover, it seems that rather than competing with each other, AI and human analysts are complementary. The novel technology can be used with great success to help us in areas where we lag, and the combined knowledge and forecasts of AI and humans outperform the AI analyst in each year.

Community Alpha of QuantConnect – Part 1: Following numerous quantitative strategies

July 1, 2021

Quantitative based community is represented by the Quantconnect – Algorithmic Trading Platform, where quants can research, backtest and trade their systematic strategies. Additionally, similar to Seeking Alpha, there is a possibility to follow other quants/analysts through the open free market – Alpha Market.
To our best knowledge, the literature on community/social media alpha is scarce, and this paper aims to fill this gap. In the first part, we evaluate the benchmark strategy that consists of all strategies in the alpha market that are equally weighted. Moreover, through multidimensional scaling and clustering analysis, we examine how well can significantly lower amount of strategies track the aforementioned benchmark. This could solve the problem of costly and inconvenient following of every strategy in the market. Overall, this approach can lead to a strategy that follows the benchmark with drastically reduced costs, and these strategies can be even more profitable and less volatile.

Stay tuned for the 2nd, 3rd and 4th part of this series, where we will step on the gas and explore factor meta-strategies built on top of the QuantConnect’s Alpha Market.

The Knowledge Graphs for Macroeconomic Analysis with Alternative Big Data

June 25, 2021

There are many known relationships among macroeconomic variables in economics, while some of them are even presented as “laws”—for example, money supply and inflation or benchmark interest rates and inflation. However, the well-known economic models usually utilize only a small amount of variables. Nowadays, with the advances in machine learning and big data fields, these established models might be improved. A possible solution is presented in the research paper of Yang et al. (2020). The authors construct knowledge graphs where they connect widely recognized variables such as GDP, inflation, etc., with other more or less known variables based on the massive textual data from financial journals and research reports published by leading think tanks, consulting firms or asset management companies. With the help of advanced natural language processing, it is possible to basically “read “all the relevant published research and find the relationships among the macroeconomic variables. While this task could take years for human readers, the machine learning method can go through these texts in a much shorter time.

Hierarchical Risk Parity

February 21, 2020

Various risk parity methodologies are a popular choice for the construction of better diversified and balanced portfolios. It is notoriously hard to predict the future performance of the majority of asset classes. Risk parity approach overcomes this shortcoming by building portfolios using only assets’ risk characteristics and correlation matrix. A new research paper written by Lohre, Rother and Schafer builds on the foundation of classical risk parity methods and presents hierarchical risk parity technique. Their method uses graph theory and machine learning to build a hierarchical structure of the investment universe. Such structure allows better division of assets into clusters with similar characteristics without relying on classical correlation analysis. These portfolios then offer better tail risk management, especially for skewed assets and style factor strategies.

Authors: Lohre, Rother and Schafer

Title: Hierarchical Risk Parity: Accounting for Tail Dependencies in Multi-Asset Multi-Factor Allocations

Alternative Fair-Value Models for Currency Value Strategy

January 17, 2020

The idea of buying an investment asset for a lower price than a fair-value is the cornerstone of value factor strategies. Various value strategies were popularized by famous investor Benjamin Graham (and his successors like Warren Buffett) and were firstly employed in the stock market. This idea of looking for investment opportunities that can be bought cheaply can also be applied in currency markets – Currency Value Factor strategy. There is, however, one catch – an investor must know the fair-value exchange rate for currencies. The most popular equilibrium exchange rate model used for this purpose is based on PPP (purchasing power parity). A new research paper written by Ca’ Zorzi, Cap, Mijakovic, and Rubaszek analyzes two additional models – Behavioral Equilibrium Exchange Rate (BEER) and the Macroeconomic Balance (MB) approach to assess which model has the best forecasting power.

Authors: Ca’ Zorzi, Cap, Mijakovic, Rubaszek

Title: The Predictive Power of Equilibrium Exchange Rate Models