As usual, at this time of the year, let us do a short recapitulation of posts on our blog in the previous 12 months. We have published over 75 short analyses of academic papers and our own research articles on this blog in 2023. We want to use this opportunity to summarize 10 of them, which were the most popular (based on the Google Analytics tool). The top 10 is really diverse; maybe you will be able to find something you have not read yet …
Nbr. 10: Price Momentum or Factor Momentum: What Leads What? – contributes to the never-ending debate of the chicken-or-egg problem of what comes first – does the stock price momentum originate from the factor momentum?
Nbr. 9: BERT Model – Bidirectional Encoder Representations from Transformers – our introduction to one of the most popular Natural Language Processing model
Nbr. 8: Optimal Market Making Models with Stochastic Volatility – shows the optimal prices for HFT to execute the limit buy and sell orders where a stochastic volatility model generates the mid prices of the assets in the market
Nbr. 7: The Seasonality of Bitcoin – this article explores potential seasonal patterns related to Bitcoin, focusing on whether these patterns are influenced by factors such as current market trends or the level of volatility in the market
Nbr. 6: An Introduction to Machine Learning Research Related to Quantitative Trading – discusses what machine learning is, how it can be used in quantitative trading, and how has the popularity of ML strategies increased over the years
Nbr. 5: An Evaluation of the Skewness Model on 22 Commodities Futures – explores the commodity skewness trading strategy and performs the battery of robustness tests to see how sensitivity analysis changes overall results
Nbr. 4: What Can We Extract From the Financial Influencers’ Advice? – what’s the real value of the Financial Influencers’ advice, and can we extract useful information from their opinions?
Nbr. 3: Avoid Equity Bear Markets with a Market Timing Strategy Part 1, 2, 3, y revisit – is our popular series of articles where our goal was to construct a market timing strategy that would reliably sidestep the equity market during bear markets
Nbr. 2: In-Sample vs. Out-Of-Sample Analysis of Trading Strategies – a test of the out-of-sample decay of trading strategies with hard data – our regularly updated database of replicated quant strategies
Nbr. 1: Can We Backtest Asset Allocation Trading Strategy in ChatGPT? & Hello ChatGPT, Can You Backtest Strategy for Me? – our hugely popular blog posts in which we tried to persuade ChatGPT to become a data analyst for us
Have a successful New Year 2024 …
Yours,
Team Quantpedia.com
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