Commodity Trading Advisors (CT As) have historically relied on trend-following rules that operate on vastly different horizons--from long-term breakouts that capture major directional moves to short-term momentum signals that thrive in fast-moving markets. Despite a large body of work on trend following, the relative merits and interactions of short-versus long-term trend systems remain controversial. This paper adds to the debate by (i) dynamically decomposing CT A returns into short-term trend, long-term trend and market beta factors using a Bayesian graphical model, and (ii) showing how the blend of horizons shapes the strategy's risk-adjusted performance.

Generative artificial intelligence (GenAI)--a set of advanced technologies capable of generating texts, images, videos, programming codes, or arts from instructions via sounds or texts--has taken the society by storm and exerted wide-range influences on many aspects of the world economy (Baldassarre et al. 2023; Mannuru et al. 2023; Sætra 2023). Although it had been around for years, GenAI came to public prominence since the introduction of ChatGPT in November 2022, a chatbox able to generate answers, reasoning, and conversations at human level. Since its introduction, ChatGPT and similar large language models have quickly made their ways into the investment industry. One common use of ChatGPT for investment is to give trading recommendations directly from news about a company (such as news articles or corporate communications) (Lopez-Lira and Tang 2023). A less direct approach is to rely on similar pretrained language models such as BERT (Devlin et al. 2018) and OPT (Zhang et al. 2022) to generate a sentiment score for each company which is then used to make trading decisions.

In traditional quantitative trading practice, navigating the complicated and dynamic financial market presents a persistent challenge. Former machine learning approaches have struggled to fully capture various market variables, often ignore long-term information and fail to catch up with essential signals that may lead the profit. This paper introduces an enhanced transformer architecture and designs a novel factor based on the model. By transfer learning from sentiment analysis, the proposed model not only exploits its original inherent advantages in capturing long-range dependencies and modelling complex data relationships but is also able to solve tasks with numerical inputs and accurately forecast future returns over a period. This work collects more than 5,000,000 rolling data of 4,601 stocks in the Chinese capital market from 2010 to 2019. The results of this study demonstrated the model's superior performance in predicting stock trends compared with other 100 factor-based quantitative strategies with lower turnover rates and a more robust half-life period. Notably, the model's innovative use transformer to establish factors, in conjunction with market sentiment information, has been shown to enhance the accuracy of trading signals significantly, thereby offering promising implications for the future of quantitative trading strategies.

Designing an optimum portfolio for allocating suitable weights to its constituent assets so that the return and risk associated with the portfolio are optimized is a computationally hard problem. The seminal work of Markowitz that attempted to solve the problem by estimating the future returns of the stocks is found to perform sub-optimally on real-world stock market data. This is because the estimation task becomes extremely challenging due to the stochastic and volatile nature of stock prices. This work illustrates three approaches to portfolio design minimizing the risk, optimizing the risk, and assigning equal weights to the stocks of a portfolio. Thirteen critical sectors listed on the National Stock Exchange (NSE) of India are first chosen. Three portfolios are designed following the above approaches choosing the top ten stocks from each sector based on their free-float market capitalization. The portfolios are designed using the historical prices of the stocks from Jan 1, 2017, to Dec 31, 2022. The portfolios are evaluated on the stock price data from Jan 1, 2022, to Dec 31, 2022. The performances of the portfolios are compared, and the portfolio yielding the higher return for each sector is identified.

Portfolio optimization has been an area that has attracted considerable attention from the financial research community. Designing a profitable portfolio is a challenging task involving precise forecasting of future stock returns and risks. This chapter presents a comparative study of three portfolio design approaches, the mean-variance portfolio (MVP), hierarchical risk parity (HRP)-based portfolio, and autoencoder-based portfolio. These three approaches to portfolio design are applied to the historical prices of stocks chosen from ten thematic sectors listed on the National Stock Exchange (NSE) of India. The portfolios are designed using the stock price data from January 1, 2018, to December 31, 2021, and their performances are tested on the out-of-sample data from January 1, 2022, to December 31, 2022. Extensive results are analyzed on the performance of the portfolios. It is observed that the performance of the MVP portfolio is the best on the out-of-sample data for the risk-adjusted returns. However, the autoencoder portfolios outperformed their counterparts on annual returns.

A pair-trading strategy is an approach that utilizes the fluctuations between prices of a pair of stocks in a short-term time frame, while in the long-term the pair may exhibit a strong association and co-movement pattern. When the prices of the stocks exhibit significant divergence, the shares of the stock that gains in price are sold (a short strategy) while the shares of the other stock whose price falls are bought (a long strategy). This paper presents a cointegration-based approach that identifies stocks listed in the five sectors of the National Stock Exchange (NSE) of India for designing efficient pair-trading portfolios. Based on the stock prices from Jan 1, 2018, to Dec 31, 2020, the cointegrated stocks are identified and the pairs are formed. The pair-trading portfolios are evaluated on their annual returns for the year 2021. The results show that the pairs of stocks from the auto and the realty sectors, in general, yielded the highest returns among the five sectors studied in the work. However, two among the five pairs from the information technology (IT) sector are found to have yielded negative returns.

Using logical clauses to represent patterns, Tsetlin machines (TMs) have recently obtained competitive performance in terms of accuracy, memory footprint, energy, and learning speed on several benchmarks. A team of Tsetlin automata (TAs) composes each clause, thus driving the entire learning process. These are rewarded/penalized according to three local rules that optimize global behaviour. Each clause votes for or against a particular class, with classification resolved using a majority vote. In the parallel and asynchronous architecture that we propose here, every clause runs in its own thread for massive parallelism. For each training example, we keep track of the class votes obtained from the clauses in local voting tallies. The local voting tallies allow us to detach the processing of each clause from the rest of the clauses, supporting decentralized learning. Thus, rather than processing training examples one-by-one as in the original TM, the clauses access the training examples simultaneously, updating themselves and the local voting tallies in parallel. There is no synchronization among the clause threads, apart from atomic adds to the local voting tallies. Operating asynchronously, each team of TA will most of the time operate on partially calculated or outdated voting tallies. However, across diverse learning tasks, it turns out that our decentralized TM learning algorithm copes well with working on outdated data, resulting in no significant loss in learning accuracy. Further, we show that the approach provides up to 50 times faster learning. Finally, learning time is almost constant for reasonable clause amounts. For sufficiently large clause numbers, computation time increases approximately proportionally. Our parallel and asynchronous architecture thus allows processing of more massive datasets and operating with more clauses for higher accuracy.

The Major League Baseball team, the Oakland Athletics, for many years a poorly performing and unsuccessful team, surprised everybody during the 2000-2001 season. The team had a remarkable run of wins, led the Major League in performance, and broke a number of League records. Michael Lewis, in his bestselling book, "Moneyball: The Art of Winning an Unfair Game", describes the secret to this turnaround. The A's new, young and unexperienced manager, Billy Beane, "revolutionized" the team's roster by releasing a number of star players (despite the protests of his closest advisors) and instead, signing a number of unknown players. He chose these players by using a model/algorithm that was based on several professional, well-defined baseball parameters.

I've spent a majority of my adult life in investing. Recently I became more interested in approaching the topic from a quantitative angle. The promise of automating an investment approach whilst I sit on a beach sipping sangria's was all too compelling to ignore. With Sir Isaac's expression in my mind I thought what better place to start than existing research papers. I thought hopefully they'll give me some unique knowledge that I can build up on when I write my own strategies.

Artificial intelligence is rapidly evolving. Unprecedented advances in machine and deep learning have even called for some concern. Elon Musk, futurist billionaire and CEO of SpaceX and Tesla Motors, has dubbed it mankind's "greatest existential threat." Indeed, driverless cars, a technology Musk himself is developing, would displace up to 15 percent of the world's workers – a figure the Tesla CEO provided himself. The world of finance is by no means immune to the disruption AI will cause. In fact, artificial intelligence is already changing the way we invest.