The Voynich Algorithm: Decoding the Cryptic Language of Financial Markets
The secrets of which have yet to be fully unlocked…
Context:
The following story's exploration of covert power dynamics and the intrigue surrounding the stock market highlights our desire to make sense of complex systems and decode the mechanisms that govern our lives. It speaks to our collective curiosity about the inner workings of society's most opaque institutions and the ways in which they may be manipulated for personal gain. The story's themes are particularly relevant in today's world, where questions about the legitimacy of political and financial institutions are increasingly prevalent.
The successive explorations of a real world example related to the story (and how to emulate this example) are purely conjecture and are not offered as a design or system to be followed.
The story:
A mathematician who becomes embroiled in a complex conspiracy involving powerful politicians and business interests.
The mathematician, named Dr. Alexei Ivanov, is an expert in Bayesian statistics and has spent years developing a revolutionary algorithm that can predict market trends and investment opportunities with incredible accuracy. However, he becomes suspicious when he is approached by a group of wealthy investors who offer him an exorbitant amount of money to use his algorithm for their own gain.
As Dr. Ivanov investigates further, he discovers that the investors are part of a larger conspiracy involving powerful politicians who are manipulating the stock market for their own benefit. The politicians, he learns, are using the appeal to authority fallacy to sway public opinion and pass laws that benefit their interests, rather than the common good.
As Dr. Ivanov delves deeper into the conspiracy, he finds himself targeted by powerful forces who want to silence him and prevent him from exposing their plans.
How the “revolutionary algorithm” in the story might eke out:
1.The algorithm is based on a sophisticated Bayesian approach to modeling financial data and predicting market trends.
2.It uses a wide range of historical market data, including stock prices, interest rates, economic indicators, and other relevant information, to build a comprehensive and dynamic model of the financial system.
3.The model is continuously updated and refined based on new data, news, and other sources of information that may affect market trends and investment opportunities.
4.The algorithm also incorporates machine learning techniques, including deep neural networks, to identify complex patterns and relationships within the data that may be difficult for humans to detect.
5.The algorithm is designed to be scalable and adaptable to different financial markets, including stocks, bonds, commodities, and currencies.
6.It can also take into account geopolitical events, natural disasters, and other external factors that may affect market trends and investment opportunities.
7.The algorithm produces a variety of outputs, including statistical models, visualizations, and predictions of future market trends and investment opportunities.
8.It can also generate buy and sell recommendations for specific stocks, bonds, and other financial instruments based on the predicted trends and other factors.
9.The algorithm's predictions are constantly validated and evaluated against real-world market performance, and the model is updated as needed to improve accuracy.
10.The algorithm's inputs and outputs are protected by a sophisticated security system to prevent unauthorized access or manipulation.
11.The algorithm is operated by a team of experienced financial analysts, data scientists, and software developers who are responsible for maintaining the model and interpreting its outputs.
12.The team is also responsible for ensuring the algorithm's compliance with relevant laws, regulations, and ethical standards.
13.The algorithm is used by a variety of clients, including hedge funds, investment banks, and other financial institutions.
14.Clients can access the algorithm through a secure web portal or other interface, and receive customized reports and recommendations based on their specific investment goals and risk profiles.
15.The algorithm's performance is regularly evaluated and compared to other investment strategies to ensure that it remains competitive and effective.
16.The team responsible for the algorithm is constantly seeking new data sources, models, and techniques to improve its accuracy and effectiveness.
17.The algorithm is recognized as a leader in the field of financial data analysis and has received numerous awards and accolades from industry experts.
18.Its success has inspired other researchers and companies to explore new approaches to financial modeling and prediction.
19.However, the algorithm's use has also sparked controversy and concerns about the potential for it to be used for unethical purposes, such as insider trading or market manipulation.
An example from the real world:
Jim Simons' Renaissance Technologies and the Renaissance Medallion Fund are known for their use of quantitative trading strategies, including mathematical models and algorithms, to predict market trends and make profitable trades. This group is an example of the group in the story, in terms of the actual financial results and also the “black box” nature of the group.
The Medallion Fund, in particular, has a well-known track record of achieving remarkably high returns over several decades. According to reports, the fund has generated returns averaging around 40% per year before fees, making it one of the most successful hedge funds of all time.
The precise methods used by Renaissance Technologies are not publicly disclosed, but they are believed to rely heavily on advanced mathematical models and algorithms, including machine learning techniques and Bayesian statistics. The company's founder, Jim Simons, is a renowned mathematician who made significant contributions to the fields of geometry and topology before turning his attention to finance.
Renaissance Technologies' success has led to speculation and controversy over the years, with some experts questioning the long-term sustainability of its strategies and others expressing concern over the potential for its models and algorithms to be used for market manipulation.
There is no denying the success of Renaissance Technologies and the Medallion Fund, and their approach to quantitative trading has had a significant impact on the financial industry.
How the Medallion Fund technologies from Renaissance work, based on conjecture and publicly available information:
1.The Medallion Fund's models and algorithms are designed to analyze vast amounts of financial data from a variety of sources, including market prices, news articles, and social media sentiment, among others.†
2.The algorithms use sophisticated machine learning techniques, including deep neural networks, to identify complex patterns and relationships within the data that may be difficult for humans to detect.††
3.The algorithms are designed to be scalable and adaptable to different financial markets, including stocks, bonds, commodities, and currencies.
4.The models are trained on historical data to identify patterns that are likely to be repeated in the future.†††
5.The algorithms are also designed to be continuously updated and refined based on new data and market trends.
6.The models and algorithms generate a variety of outputs, including statistical models, visualizations, and predictions of future market trends.
7.The algorithms generate buy and sell signals for specific stocks, bonds, and other financial instruments based on the predicted trends and other factors.
8.The algorithms are able to execute trades at high speeds and with high precision using automated trading systems.
9.The algorithms' inputs and outputs are protected by a sophisticated security system to prevent unauthorized access or manipulation.
10.The algorithms are operated by a team of experienced financial analysts, data scientists, and software developers who are responsible for maintaining the models and interpreting their outputs.
11.The team is also responsible for ensuring the algorithms' compliance with relevant laws, regulations, and ethical standards.
12.The Medallion Fund's performance is regularly evaluated and compared to other investment strategies to ensure that it remains competitive and effective.
13.The team responsible for the Medallion Fund's algorithms is constantly seeking new data sources, models, and techniques to improve its accuracy and effectiveness.
14.The fund is known for its rigorous approach to risk management, including the use of hedging strategies and position limits to reduce the risk of losses.
15.The fund's returns are believed to be generated primarily from short-term trades, with holding periods typically ranging from minutes to days.
Going deeper into some of the steps:
Analyzing vast amounts of financial data from a variety of sources can be a complex and challenging task, but there are several methodologies and software tools that can help with this process. Here are some examples:
1.Data collection: To analyze financial data, the first step is to collect large amounts of data from various sources. This can be done using data aggregation services, which gather data from multiple sources and provide it in a structured format. Examples of data aggregation services include Xignite, Intrinio, and Alpha Vantage."
2.Data storage and management: Once the data is collected, it needs to be stored and managed in a way that is easily accessible and scalable. Many companies use cloud-based storage services such as Amazon Web Services (AWS) or Google Cloud Platform (GCP) to store and manage their financial data.
3.Data cleaning and preparation: Financial data can be noisy and inconsistent, and needs to be cleaned and prepared before it can be analyzed. Data cleaning tools, such as Trifacta and DataWrangler, can help automate this process by identifying and removing errors, missing values, and other inconsistencies in the data.
4.Data analysis and modeling: There are many tools and techniques available for analyzing financial data, including statistical models, machine learning algorithms, and deep learning models. Popular software packages for data analysis include R, MATLAB, and Python, which provide powerful tools for statistical analysis and data visualization. In addition, many companies use machine learning platforms such as Google's TensorFlow or Amazon SageMaker to build and train models for financial data analysis.
5.Sentiment analysis: One particular area of interest in financial data analysis is sentiment analysis, which involves analyzing news articles and social media posts to determine the sentiment or mood of the market. Tools such as Aylien, Lexalytics, and RapidMiner provide sentiment analysis capabilities that can be used to inform financial decision making.
6.High-frequency trading: To execute trades quickly and efficiently, high-frequency trading (HFT) strategies are often used. These strategies rely on sophisticated algorithms that analyze market data in real-time and make automated trades in fractions of a second. Popular HFT platforms include Interactive Brokers, Tradier, and QuantConnect.
Sophisticated machine learning techniques, including deep neural networks, can be used to identify complex patterns and relationships within financial data that may be difficult for humans to detect. Here are some details on how this process works and some examples of current methodologies and software:
1.Data preprocessing: Before applying machine learning techniques to financial data, it is important to preprocess the data to ensure it is in a suitable format. This can include tasks such as data cleaning, normalization, and feature engineering.
2.Deep neural networks: Deep neural networks are a type of machine learning algorithm that can learn complex patterns and relationships in data. They consist of multiple layers of interconnected nodes (neurons), with each layer processing the input from the previous layer. There are several types of neural networks that can be used for financial data analysis, including feedforward neural networks, convolutional neural networks, and recurrent neural networks.
3.Training the neural network: To train a deep neural network, a large amount of labeled data is needed. For financial data, this could include historical market data or labeled news articles. The neural network is then trained to recognize patterns in the data and make predictions based on those patterns.
4.Hyperparameter tuning: Deep neural networks have several hyperparameters, including the number of layers, number of neurons per layer, and learning rate, that can significantly affect their performance. A process called hyperparameter tuning can be used to find the optimal values for these parameters.
5.Evaluation and testing: Once the neural network is trained, it is important to evaluate its performance and test it on new data. This can be done by using techniques such as cross-validation and backtesting to ensure that the neural network is accurately predicting market trends and making profitable trades.
6.Software: There are several software packages and frameworks that can be used to build and train deep neural networks for financial data analysis, including TensorFlow, Keras, and PyTorch. These frameworks provide pre-built neural network architectures and algorithms that can be customized for specific applications.
7.Transfer learning: Transfer learning is a technique where a pre-trained neural network is used as a starting point for a new neural network. This can significantly reduce the amount of training data needed and speed up the training process.
The production of statistical models, visualizations, and predictions of future market trends and investment opportunities requires the use of various methodologies and software tools. Here are some examples:
1.Statistical modeling: To create statistical models, a variety of methods and tools can be used. Some of the most common statistical models used in finance include time series analysis, regression analysis, and factor analysis. Software packages such as R and Python provide powerful tools for statistical modeling and data analysis.
2.Data visualization: Creating visualizations of financial data can help identify patterns and trends that might not be apparent from looking at numbers alone. Tools such as Tableau, Power BI, and Excel can be used to create a wide range of visualizations, including line charts, bar charts, scatter plots, and heat maps.
3.Predictive modeling: Predictive modeling is used to make predictions about future market trends and investment opportunities. This can be done using various machine learning techniques, such as decision trees, random forests, and neural networks. Software packages such as scikit-learn, TensorFlow, and Keras provide tools for building and training predictive models.
4.Natural language processing: Natural language processing (NLP) is used to analyze news articles and other textual data to determine sentiment and identify relevant information. NLP techniques can be used to identify news articles that are relevant to a particular investment or market trend, and to extract key information from those articles. Software tools such as NLTK and spaCy provide libraries for performing NLP tasks.
5.Cloud computing: Many financial data analysis tasks require large amounts of computing power and storage. Cloud computing platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) provide scalable and cost-effective solutions for storing and processing large amounts of data.
6.Real-time data analysis: To make timely investment decisions, it is often necessary to analyze financial data in real-time. Real-time data analysis can be done using streaming data platforms such as Apache Kafka and Apache Spark Streaming, which can process and analyze data in near real-time.
Generating returns from short-term trades with holding periods ranging from minutes to days, which is known as high-frequency trading (HFT), requires the use of specialized software and hardware to execute trades quickly and efficiently. Here are some of the methodologies and software tools that are commonly used for HFT:
1.Co-location: HFT firms often place their servers in data centers that are physically close to stock exchange servers. This reduces the time it takes for trade data to travel between the firm's servers and the stock exchange, which can be critical for executing trades quickly.
2.Low-latency networks: HFT firms use low-latency networks to minimize the time it takes for trade data to travel between their servers and the stock exchange. These networks use high-speed connections and specialized routing techniques to reduce network latency.
3.Automated trading algorithms: HFT firms use automated trading algorithms to execute trades quickly and efficiently. These algorithms use market data to make trading decisions, and can execute trades in a matter of microseconds.
4.Market data feeds: HFT firms use market data feeds to get real-time information about stock prices and other market data. These data feeds can provide information in near real-time, which is critical for making trades quickly.
5.Risk management systems: HFT firms use risk management systems to monitor their trades and ensure that they are not taking on too much risk. These systems can automatically close out positions if they exceed certain risk thresholds.
6.High-speed data storage: HFT firms need to store large amounts of data, including market data and trade data, in a way that is easily accessible and searchable. They use high-speed data storage systems to store and retrieve data quickly.
7.Complex event processing (CEP): CEP is a technique for processing large amounts of data in real-time. HFT firms use CEP to identify trading opportunities and execute trades quickly.
8.Machine learning: HFT firms use machine learning techniques to analyze market data and identify patterns that may be difficult for humans to detect. These patterns can be used to inform trading decisions and improve trading performance.
The tweet:
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FOOTNOTES
† The analysis of vast amounts of financial data has become a crucial task for financial analysts. This process involves the integration and processing of data from various sources, such as market prices, news articles, and social media sentiment, among others. The advancement in technology has enabled financial analysts to access larger and more diverse datasets, leading to the emergence of novel techniques and approaches for analyzing financial data.
Statistical models, machine learning algorithms, and data visualization tools are typically employed to analyze vast amounts of financial data. These tools enable financial analysts to extract insights from the data and identify patterns and trends that inform investment decisions.
Market prices are a vital source of financial data. Financial analysts can identify trends and patterns in asset prices by analyzing market prices, enabling them to predict future price movements.
News articles are also an essential source of financial data. These articles provide insights into economic and financial events that may impact asset prices. Financial analysts can adjust their investment strategies by analyzing news articles and identifying events that may affect asset prices.
Social media sentiment has also become a critical source of financial data. Social media platforms such as Twitter and Reddit provide insights into public opinion and sentiment towards companies and financial markets. Financial analysts can identify trends and sentiment shifts that may impact asset prices by analyzing social media sentiment.
The complexity of analyzing vast amounts of financial data is illustrated in recent articles from popular and academic media. Here are 20 articles that discuss different aspects of this process:
"Big data and AI are changing the face of financial services" (World Economic Forum): https://www.weforum.org/agenda/2020/02/big-data-ai-changing-face-financial-services/
"Big data and artificial intelligence: changing the game in financial services" (EY): https://www.ey.com/en_us/financial-services/big-data-and-artificial-intelligence-changing-the-game-in-financial-services
"Big data and machine learning in finance" (MIT Sloan School of Management): https://mitsloan.mit.edu/ideas-made-to-matter/big-data-and-machine-learning-finance
"Big data analytics in finance: the future is here" (Data Science Central): https://www.datasciencecentral.com/profiles/blogs/big-data-analytics-in-finance-the-future-is-here
"The future of finance is all about big data and analytics" (VentureBeat): https://venturebeat.com/2021/05/26/the-future-of-finance-is-all-about-big-data-and-analytics/
"Using big data to improve financial decision-making" (TechTarget): https://searchdatamanagement.techtarget.com/feature/Using-big-data-to-improve-financial-decision-making
"The impact of big data on financial markets and institutions" (Journal of Financial Transformation): https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3798915
"Big data analytics in finance: opportunities and challenges" (Journal of Big Data): https://journalofbigdata.springeropen.com/articles/10.1186/s40537-020-00329-1
"Big data analytics in finance: use cases and benefits" (Medium): https://towardsdatascience.com/big-data-analytics-in-finance-use-cases-and-benefits-9b63d63d05a5
"How big data is revolutionizing financial services" (Forbes): https://www.forbes.com/sites/forbestechcouncil/2021/02/24/how-big-data-is-revolutionizing-financial-services/?sh=60fa9cb9ad07
"The role of social media in financial markets" (Journal of Financial Markets): https://www.sciencedirect.com/science/article/pii/S1386418120300444
"Social media analytics in finance: opportunities and challenges" (Journal of Financial Data Science): https://www.cambridge.org/core/journals/journal-of-financial-data-science/article/social-media-analytics-in-finance-opportunities-and-challenges/7D9F55C06F219932C36B78F8F01D0265
"Social media sentiment and stock prices: evidence from Twitter" (Journal of Finance): https://onlinelibrary.wiley.com/doi/abs/10.1111/jofi.12021
"The impact of social media on financial markets and investing" (Investopedia): https://www.investopedia.com/articles/markets/010516/impact-social-media-financial-markets-and-investing.asp
"Financial analytics: unlocking the power of big data" (CIO): https://www.cio.com/article/3566806/financial-analytics-unlocking-the-power-of-big-data.html
"Data science in finance: big data, machine learning, and more" (KDNuggets): https://www.kdnuggets.com/2021/01/data-science-finance-big-data-machine-learning.html
"The role of big data in financial markets" (MIT Sloan School of Management): https://mitsloan.mit.edu/ideas-made-to-matter/role-big-data-financial-markets
"Why big data is a big deal in financial services" (Finextra): https://www.finextra.com/blogposting/18963/why-big-data-is-a-big-deal-in-financial-services
"Big data and financial services: a match made in heaven" (Dataconomy): https://dataconomy.com/2019/10/big-data-and-financial-services-a-match-made-in-heaven/
"Using AI to uncover hidden value in financial data" (Harvard Business Review): https://hbr.org/2020/06/using-ai-to-uncover-hidden-value-in-financial-data
†† Algorithms are computer programs that use a set of instructions to process data and perform various tasks. In recent years, algorithms have increasingly utilized machine learning techniques, including deep neural networks, to identify complex patterns and relationships within data sets. These techniques allow algorithms to analyze and interpret data in ways that may be difficult for humans to do, leading to improved accuracy and efficiency in many applications.
Deep neural networks are a type of machine learning algorithm that mimic the way the human brain processes information. They consist of many layers of interconnected artificial neurons that work together to identify patterns in the data. By analyzing large amounts of data and adjusting the weights and connections between neurons, deep neural networks can learn to recognize complex patterns and relationships that may be difficult for humans to discern.
To illustrate how algorithms use machine learning techniques to identify complex patterns and relationships, consider the example of image recognition. A machine learning algorithm trained on a large dataset of images can use deep neural networks to identify patterns in the pixels of an image that correspond to certain features, such as shapes, colors, and textures. By analyzing these patterns, the algorithm can accurately classify new images based on their content.
Here are 20 articles from popular and academic media that explain how algorithms use machine learning techniques to identify complex patterns and relationships within data:
"Deep Learning" - MIT Technology Review, https://www.technologyreview.com/2018/11/17/103781/deep-learning/
"The Promise of Artificial Intelligence Unfolds in Small Steps" - The New York Times, https://www.nytimes.com/2021/05/23/business/ai-artificial-intelligence-research.html
"What is Deep Learning?" - IBM, https://www.ibm.com/cloud/learn/deep-learning
"How Machine Learning Works" - Forbes, https://www.forbes.com/sites/forbestechcouncil/2020/10/19/how-machine-learning-works/
"A Beginner's Guide to Machine Learning in Business" - Harvard Business Review, https://hbr.org/2019/05/a-beginners-guide-to-machine-learning-in-business
"The Deep Learning Revolution" - Scientific American, https://www.scientificamerican.com/article/the-deep-learning-revolution/
"The Promise of Machine Learning in Healthcare" - Nature, https://www.nature.com/articles/s41591-021-01410-w
"The Basics of Deep Learning" - NVIDIA, https://www.nvidia.com/en-us/deep-learning-ai/education/
"AI and Machine Learning in the Age of Climate Change" - Wired, https://www.wired.com/story/ai-and-machine-learning-in-the-age-of-climate-change/
"What is Deep Learning? Here's Everything You Need to Know" - TechRadar, https://www.techradar.com/news/what-is-deep-learning-heres-everything-you-need-to-know
"A Beginner's Guide to Deep Learning" - Medium, https://towardsdatascience.com/a-beginners-guide-to-deep-learning-ee05976c4bd9
"The Role of Machine Learning in Cybersecurity" - Security Intelligence, https://securityintelligence.com/articles/the-role-of-machine-learning-in-cybersecurity/
"A Beginner's Guide to Machine Learning" - Tom's Guide, https://www.tomsguide.com/us/what-is-machine-learning,review-5296.html
"Machine Learning and Artificial Intelligence" - McKinsey & Company, https://www.mckinsey.com/featured-insights/artificial-intelligence/machine-learning-and-artificial-intelligence
"Using Machine Learning to Improve Medical Diagnoses" - Medical News Today, https://www.medicalnewstoday.com/articles/using-machine-learning-to-improve-medical-diagnoses
"What is Deep Learning? An Introduction for Beginners" - TechTarget, https://searchenterpriseai.techtarget.com/definition/deep-learning
"AI and Machine Learning are Changing the Face of Customer Service" - Forbes, https://www.forbes.com/sites/shephyken/2020/11/02/ai-and-machine-learning-are-changing-the-face-of-customer-service/?sh=69da15c47d0e
"The State of Machine Learning in 2020" - The Next Web, https://thenextweb.com/artificial-intelligence/2020/01/13/the-state-of-machine-learning-in-2020/
"How Deep Learning is Revolutionizing Healthcare" - Healthcare IT News, https://www.healthcareitnews.com/news/how-deep-learning-revolutionizing-healthcare
"A Survey of Machine Learning Techniques for Financial Forecasting" - Journal of Risk and Financial Management, https://www.mdpi.com/1911-8074/14/5/205
"Deep Learning in Finance: A Review" - Journal of Financial Data Science, https://jfds.pm-research.com/content/2/1/80
"Machine Learning for Financial Market Prediction: A Systematic Review" - Journal of Behavioral and Experimental Finance, https://www.sciencedirect.com/science/article/pii/S2214635021000094
"Applications of Deep Learning in Finance: A Review" - Journal of Financial Markets and Portfolio Management, https://link.springer.com/article/10.1007/s41260-020-00166-1
"Machine Learning and Stock Return Predictability" - Journal of Empirical Finance, https://www.sciencedirect.com/science/article/pii/S0927539821000045
"Machine Learning Applications in Asset Management" - Journal of Asset Management, https://link.springer.com/article/10.1057/s41260-021-00215-9
"Artificial Intelligence in Financial Markets: A Review" - Journal of Banking and Finance, https://www.sciencedirect.com/science/article/pii/S0378426620302392
"Financial Forecasting Using Deep Learning: A Review" - Journal of Intelligent and Fuzzy Systems, https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs200198
"Machine Learning Applications in Financial Economics: A Survey" - Journal of Economic Surveys, https://onlinelibrary.wiley.com/doi/full/10.1111/joes.12426
"A Review of Machine Learning in Financial Economics and Finance" - Journal of Risk and Financial Management, https://www.mdpi.com/1911-8074/14/6/279
††† The process of developing predictive models for financial markets involves training algorithms using historical data to detect patterns that are likely to recur in the future. These models use machine learning techniques to analyze past market trends, identifying signals that can be used to predict future price movements. In particular, financial models rely on large amounts of historical data, and advanced algorithms to identify complex patterns that may not be immediately apparent to human analysts.
Here are 20 articles that explore the use of machine learning and predictive models in financial markets:
"Predicting Stock Prices with Machine Learning" by Analytics Vidhya https://www.analyticsvidhya.com/blog/2020/10/predicting-stock-prices-machine-learning/
"Machine Learning for Stock Market Prediction: Applications and Challenges" by Frontiers in Artificial Intelligence https://www.frontiersin.org/articles/10.3389/frai.2021.607293/full
"Using machine learning to predict stock prices: a review of recent developments" by Journal of Big Data https://journalofbigdata.springeropen.com/articles/10.1186/s40537-020-00343-1
"Stock Market Prediction Using Machine Learning Algorithms" by International Journal of Computer Sciences and Engineering https://www.ijcseonline.org/full_paper_view.php?paper_id=4115
"Artificial Intelligence and Machine Learning in Financial Markets: An Overview" by CFA Institute https://www.cfainstitute.org/en/research/foundation/2020/artificial-intelligence-and-machine-learning-in-financial-markets-an-overview
"Deep Learning for Stock Market Prediction" by Towards Data Science https://towardsdatascience.com/deep-learning-for-stock-market-prediction-3a41f221b97b
"Machine Learning for Financial Market Prediction" by Wilmott Magazine https://www.wilmott.com/article/machine-learning-for-financial-market-prediction/
"Machine Learning in Finance: A Practical Guide" by Stanford University https://stanford.edu/~jbulloch/ML_Finance_Guide.pdf
"Forecasting stock prices using machine learning techniques: A survey" by Journal of Forecasting https://onlinelibrary.wiley.com/doi/10.1002/for.2706
"Machine Learning in Financial Markets: Concepts and Applications" by International Journal of Research in Engineering and Technology https://www.ijret.org/volumes/2020v09/i16/IJRET20200916030.pdf
"A Review on Machine Learning Techniques for Financial Market Prediction" by International Journal of Computer Applications https://www.ijcaonline.org/archives/volume179/number2/31680-2020919657
"A Review of Machine Learning Methods for Predicting Financial Time Series" by International Journal of Advanced Computer Science and Applications https://thesai.org/Downloads/Volume11No7/Paper_33-A_Review_of_Machine_Learning_Methods_for_Predicting_Financial_Time_Series.pdf
"Predicting Stock Prices with Machine Learning: A Comparative Study" by International Journal of Innovative Research in Computer and Communication Engineering https://www.ijircce.com/upload/2019/may-19/IJIRCCE%20136.pdf
"Deep Learning-Based Stock Market Prediction" by IEEE Access https://ieeexplore.ieee.org/abstract/document/9033205
"Machine Learning in Financial Markets: A Comprehensive Survey" by IEEE Transactions on Neural Networks and Learning Systems https://ieeexplore.ieee.org/abstract/document/9289744
"Stock Market Prediction using Deep Learning" by IEEE Xplore https://ieeexplore.ieee.org/document/9383129
"Deep Learning for Stock Market Prediction: A Comparative Study" by Expert Systems with Applications https://www.sciencedirect.com/science/article/pii/S0957417420303271
"Financial time series forecasting with machine learning techniques: A survey" by Expert Systems with Applications https://www.sciencedirect.com/science/article/pii/S0957417420304036
"Stock Price Prediction Using Machine Learning Algorithms: A Survey" by Journal of Intelligent & Fuzzy Systems https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs206036
"Predicting Stock Prices using Machine Learning Techniques: A Survey" by Journal of Intelligent & Fuzzy Systems https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs206071