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The art of systematic financial trading evolved with an array of approaches, ranging from simple strategies to complex algorithms all relying, primary, on aspects of time-series analysis. Recently, after visiting the trading floor of a…
S&P 500 Index is one of the most sought after stock indices in the world. In particular, Information Technology Sector of S&P 500 is the number one business segment of the S&P 500 in terms of market capital, annual revenue and the number of…
Web search data are a valuable source of business and economic information. Previous studies have utilized Google Trends web search data for economic forecasting. We expand this work by providing algorithms to combine and aggregate search…
Fluctuations in stock prices are influenced by a complex interplay of factors that go beyond mere historical data. These factors, themselves influenced by external forces, encompass inter-stock dynamics, broader economic factors, various…
The importance of considering related stocks data for the prediction of stock price movement has been shown in many studies, however, advanced graphical techniques for modeling, embedding and analyzing the behavior of interrelated stocks…
Predicting future direction of stock markets using the historical data has been a fundamental component in financial forecasting. This historical data contains the information of a stock in each specific time span, such as the opening,…
Prediction of stock prices plays a significant role in aiding the decision-making of investors. Considering its importance, a growing literature has emerged trying to forecast stock prices with improved accuracy. In this study, we introduce…
In recent years, there have been unprecedented technological advances in sensor technology, and sensors have become more affordable than ever. Thus, sensor-driven data collection is increasingly becoming an attractive and practical option…
Recent advances in Artificial Intelligence (AI) have made algorithmic trading play a central role in finance. However, current research and applications are disconnected information islands. We propose a generally applicable pipeline for…
People nowadays use search engines like Google, Yahoo, and Bing to find information on the Internet. Due to explosion in data, it is helpful for users if they are provided relevant summaries of the search results rather than just links to…
In modern financial markets, news plays a critical role in shaping investor sentiment and influencing stock price movements. However, most existing studies aggregate daily news sentiment into a single score, potentially overlooking…
This article aims to propose and apply a machine learning method to analyze the direction of returns from Exchange Traded Funds (ETFs) using the historical return data of its components, helping to make investment strategy decisions through…
There are inefficiencies in financial markets, with unexploited patterns in price, volume, and cross-sectional relationships. While many approaches use large-scale transformers, we take a domain-focused path: feed-forward and recurrent…
High-frequency trading requires fast data processing without information lags for precise stock price forecasting. This high-paced stock price forecasting is usually based on vectors that need to be treated as sequential and…
One of the pillars to build a country's economy is the stock market. Over the years, people are investing in stock markets to earn as much profit as possible from the amount of money that they possess. Hence, it is vital to have a…
Financial Sentiment Analysis (FSA) traditionally relies on human-annotated sentiment labels to infer investor sentiment and forecast market movements. However, inferring the potential market impact of words based on their human-perceived…
Accurately identifying the extremal dependence structure in multivariate heavy-tailed data is a fundamental yet challenging task, particularly in financial applications. Following a recently proposed bootstrap-based testing procedure, we…
Accurate stock price prediction is crucial for investors and financial institutions, yet the complexity of the stock market makes it highly challenging. This study aims to construct an effective model to enhance the prediction ability of…
We check the claims that data from Google Trends contain enough data to predict future financial index returns. We first discuss the many subtle (and less subtle) biases that may affect the backtest of a trading strategy, particularly when…
The growth of machine-readable data in finance, such as alternative data, requires new modeling techniques that can handle non-stationary and non-parametric data. Due to the underlying causal dependence and the size and complexity of the…