You are an expert Data Analytics in a large financial investment company. The company management would like to launch an AI stock investment strategy product.
Some of the competing investment management firms have already launched AI products. The market pressure is mounting to deliver your own version.
You think that the historical data on stocks should reveal patterns of successful indicators that predict the high-performing, or “winning”, portfolios. Big data may reveal what works in the stock market.
As the first step, a large historical data set is constructed. The data set consists of stock returns and many company features driven from financial accounts such as valuation ratios, profitability ratios etc.
Data collection is already done. There are a number of points to decide upon initially:
• Data files are downloaded from different sources. They need to be merged/cleaned/organised.
• What are the particular issues related to ‘cross-sectional, time-series’ data?
Step 1:
Organize the data set, prepare the variables for standard ML algorithms. Make sure the data set is organised and prepared (scaling, normalization, cleaning, etc. done) after a careful Exploratory Data Analysis(EDA) process.
The details of the analysis of the model and the related code are to be decided via team discussion. You are free take executive decisions to select variables to drop from the data set if you do not see much use or value for the ML analysis.
Project aim is to forecast the set of stocks that are likely to be the best (and worst) FUTURE 3-Month performers at a point in time based on their features measured and recorded prior to the observed stock returns. If the model is able to pick the FUTURE WINNERS and avoid the FUTURE LOSERS, then the portfolio will outperform the other possible portfolios that are constructed with a passive decision (i.e., hold all stocks in equal weight, or buy the largest market-cap stocks).
By using the historical data available at a point in time, the model should forecast the WINNERS of the next 3-months, and an equal-weighted portfolio can be created with the predicted WINNERS.
Analogy: If the stock returns in each time interval is assumed to be the results of a horse race, the objective is to find an ML model to predict the winning horses by using the information available prior to the horse race. The historical data set allows to Train the models and Test them to check the prediction success achieved in the past.
As a part of the required output, model has to demonstrate the performance of the ML-driven stock picking strategy with a back-test. That is to show the historical success of selected method as an investment strategy to answer the following: If someone had actually used our ML methods to construct portfolios in the past with the information that was available back then, and repeated the prediction process over time for many “horse races” what would be the performance?
Target Variable: Forward 3-Month return WINNERS (best performing stocks in the next 3-month period)
Features: Financial Ratios, Past Returns, Sector or Industry Group etc. available in the provided data sets
Questions to discuss and answer:
How to define the WINNERS and/or LOSERS at each period? (Rank them?)
How to design the prediction model? Is this a Classification problem or a Regression problem?
How to select TRAIN, TEST and VALIDATION sets? Should a moving-window approach be used?
Are there redundant features that can be discarded? How to decide which ones?
Step 2:
• Run selected ML method(s) to identify the features that have the greatest importance (and significance) to predict future returns.
• Present the results of the selected ML approach along with a description of your method.
• How do results change when different train-test samples are used? Run selected ML procedure over moving windows of time (such as moving 3-year window for the training sample, and subsequent period for the test sample).
• Do the “useful features” change over time with the moving sub-samples? Show how the set of “important features” (if any) change over moving-window samples. Show if there are any consistently useful features to predict the future Winner and Loser stock groups.
• Are there any features that could be proposed to use consistently for the new AI-driven product? Are the ML model results statistically or economically convincing? Briefly explain.
Step 3:
Create an animated chart and/or dynamic dashboard that show the changes in features importance as your Train-Test sample moves over time. (The results of the moving-window model fitting.
How is feature importance measured? Which method is more useful or more interpretable in this case?
Explore the details of methods such as Shapley Value, LIME (Local Interpretable Model-Agnostic Explanations), Partial Dependence Plots (PDP), Breakdown.
Create a teaching note on commonly used Feature Importance measures and their use cases.
Step 4.
Show the stock portfolio of stocks held based on selected ML-based model forecasts by the end of 2023. Which stocks would be selected (Top 50 [or 40] Predicted Winners) to be in the portfolio by 2023-December?
Assume you created an equal-weighted portfolio of Forecasted Top 50 [or 40] Winner Stocks in 2023-Dec. Collect the stock price/return data for the 2024-Jan to 2024-Mar period and show how your portfolio has performed compared to a market benchmark index. What would be the over/under-performance in the first quarter of 2024?
Step 5.
Apply a different AI strategy.
(i) Ask GPT what to buy among your stock universe by the end of 2023 based on the information available by the end of 2023. See how GPT’s portfolio would have performed relative to your ML-based portfolio and relative to the market benchmark in the first 3 months of 2024.
(ii) Repeat (i) for the portfolios constructed by the end of 2022.
Make sure to record your GPT prompts and the responses that you used for portfolio construction.
Based on (i, ii) who wins? GPT, or selected ML model?
Do you think LLMs can help with portfolio strategy? Why or why not?
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A simple example of preliminary GPT prompt for Step 5:
“I will give you a list of stock tickers. Make a guess or a forecast about the percent return of these stocks over the next 3 months by suggesting a predictive model or by creating a story to justify your stock preferences. Use the latest data, news and information. Suggest a portfolio of 4 stocks based on your model and/or story. Here are the tickers: AMZN, MSFT, XOM, JJ, C, JPM, PFE, AA, K, HD, NVDIA, TSLA”
…..
“Now, assume you have information and historical data only and only up to 2022-January. You do not know anything beyond the calendar year of 2022. Eliminate all information that is dated beyond the year 2022. What would be your portfolio decision then?
Try similar prompts and variants for your stock universe. Use different iterations, back-and-forth prompt conversations to decide on the useful form of prompting the LLM model.
Data:
[login to view URL]!Aj89N01PTfKFb9CB5nYcUvLgOhw?e=ou0U1p
[login to view URL]!Aj89N01PTfKFbj1gz2jfHnIv5MA?e=2CIWKr
As a seasoned data professional in the finance sector, specializing in financial analysis and statistical modeling, I'd like to offer my expertise to help you develop your AI-driven stock investment strategy. Over the course of my 7-year career, I've honed my skills in organizing, cleaning, and normalizing large datasets, making them ready for ML algorithms - a critical step required for your project. I also have extensive experience in Exploratory Data Analysis (EDA) and model selection that will enable me to identify redundant variables for exclusion from the analysis.
Moreover, I truly grasp the mission at hand. My proficiency in designing and executing predictive models using machine learning techniques aligns perfectly with your needs. As someone who has worked extensively with moving-window approaches and understands the importance of distinguishing between winning and losing stocks, I know exactly how to tackle both classification and regression aspects of this project - selecting TRAIN, TEST and VALIDATION sets effectively.
✅ Proposal for AI-Driven Stock Investment Strategy Development:
As an expert Data Analytics professional with a proven track record in financial investment, I am well-equipped to lead the AI-driven stock investment strategy project. With experience in organizing and analyzing large datasets, conducting thorough Exploratory Data Analysis, and implementing ML algorithms, I am ready to tackle the challenges outlined in the project description.
My expertise in merging, cleaning, and organizing data files from various sources, along with my proficiency in dealing with cross-sectional, time-series data issues, makes me a strong candidate for Step 1 of the project. I am skilled in preparing variables for ML algorithms, scaling, normalization, and feature selection.
For Step 2, I have experience running ML methods to identify important features for predicting future returns. I am adept at analyzing results across different train-test samples and moving time windows, ensuring robust and reliable model performance.
In Step 3, I can create dynamic dashboards to visualize changes in feature importance over time, utilizing methods such as Shapley Value and Partial Dependence Plots. My ability to interpret feature importance measures and communicate complex concepts effectively will be valuable for creating teaching notes on the subject.
For Step 4, I am prepared to construct stock portfolios based on ML forecasts and evaluate their performance against market benchmarks. I will ensure comprehensive analysis of the portfolios performance and provide insights into any over/under-performance observed.
In Step 5, I am ready to explore alternative AI strategies, including leveraging GPT prompts for portfolio construction. By comparing the performance of GPT-generated portfolios with ML-based portfolios, I will determine the most effective approach for stock selection.
Overall, my experience and skills align closely with the requirements of the project, enabling me to drive the development of an innovative AI stock investment strategy product successfully.
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With my consolidated experience in Finance and Data Analytics, I'm more than equipped to tackle your challenging project. Excel and Statistics are my bread and butter, which is why I'm confident I can organically manage complex datasets like your cross-sectional time-series data with ease. My numerous experiences in financial planning and working with large datasets means I'm deeply knowledgeable about merging, cleaning, scaling, and normalizing data.
Regarding the analysis phase, I've handled several exploratory data analysis projects and ML algorithms in my career. What separates me from others is my ability to make executive decisions on variable selections for improved ML analysis. This comes from a keen understanding of stock market nuances and an eye trained at identifying valuable indicators. Undoubtedly the winners vs losers distinction may not always be easy but my robust statistical background allows me to handle any recalibration needs with confidence.
Lastly but not limitedly, my strong suit is also in visualizing complex financial data with tools like Power BI; meaning not only will you get dependable forecasting models but also engaging visualizations that drive easy interpretation. I don't just stop at finishing the project but deliver post-project assistance too, this ensures that any kinks or feedback can be addressed promptly.
Hello,
I understand that you are looking to develop an AI-driven stock investment strategy by analyzing historical data to predict future stock performance. My approach will start with organizing and cleaning the data to prepare it for machine learning algorithms. I'll conduct exploratory data analysis to identify valuable features, while also addressing issues associated with cross-sectional and time-series data. We will define criteria for identifying winning and losing stocks and design a suitable prediction model. Continuous monitoring of feature importance across different training and testing periods will ensure our model remains robust. Additionally, I will create dynamic visualizations to demonstrate changes in feature significance, and I will perform back-testing to assess historical performance. Let's ensure that our strategy stands out in a competitive market.
What are your specific goals for evaluating the performance of the model during the back-testing phase?
Thanks,
Muhammad Awais
Hey employer,
I hold a Master's degree in Economics and statistics making me a suitable person for your project " AI-Driven Stock Investment Strategy Development ". I have more than 3 years of professional experience in statistical analysis. Besides, I have skills in Excel, Financial Analysis, Mathematics, Statistics and Statistical Analysis.
On Freelancer.com, I seek to help individuals, groups, or companies solve their statistical problems in various types of disciplines. My previous experience includes, but is not limited to: Descriptive Statistics, Visual Representation, Regression Analysis, Analysis of Variance, Nonparametric Statistics, etc.
Click on my profile, (https://www.freelancer.com/hireme/monicawriter99) to see my profile reviews as well as have an insight into what I will do for you.
Thank you.
Dear client, I extend a warm welcome and invite you to explore the best terms of service tailored to meet your needs on your project" AI-Driven Stock Investment Strategy Development". Feel free to engage in negotiations for a more favorable arrangement. Rest assured, my commitment is to deliver comprehensive, detailed, exceptional, and high-quality results well before your specified deadline.
Looking forward to the possibility of working together and exceeding your expectations.
Thank you.
Hello, I have extensive experience in data analysis, financial modeling, and machine learning, making me an ideal candidate for your AI-driven stock investment strategy project. I will start by organizing and cleaning the historical stock data, performing exploratory data analysis (EDA), and preparing it for machine learning algorithms. I will help define the WINNERS and LOSERS, design the prediction model, and select key features to forecast the best-performing stocks. Additionally, I will run various models, analyze their performance, and provide an insightful report with the results. Using techniques like Shapley values and LIME, I will highlight the most significant features. Lastly, I’ll demonstrate portfolio performance, back-test the strategy, and even compare it to GPT-based models.
-------------- NO WIN, NO PAY --------------
❇ Check out my 5-star Reviews & Portfolio from my profile:
✅https://www.freelancer.com/u/dfordeveloperss,
Looking forward to the opportunity of working together, Thank you in advance!
Hi, Hope you are doing well.
With 8+ years of experience in financial data analysis and machine learning, I am confident in delivering actionable insights.
Key Expertise:
• Financial Data Processing
• Predictive Modeling
• Machine Learning Algorithms
• Portfolio Optimization
• Data Visualization
I will begin by organizing and preparing historical stock data, ensuring high-quality inputs for ML algorithms. The model will be designed to identify future high-performing stocks using robust statistical techniques. I will also conduct back-testing to validate strategy effectiveness.
Additionally, I will explore AI-driven portfolio selection strategies, comparing ML-based stock predictions with GPT-driven recommendations. The results will be visualized using interactive dashboards for better decision-making.
Looking forward to discussing further.
Regards,
Adnan.
Hello Luke,
I am excited to assist in developing your AI-driven stock investment strategy. With my expertise in data analytics and financial modeling, I can effectively organize, clean, and prepare your large historical dataset for machine learning algorithms. This includes performing a thorough Exploratory Data Analysis (EDA) to ensure optimal variable selection and analysis.
For Step 1, I will focus on scaling, normalization, and addressing any cross-sectional or time-series issues. Post-EDA, I will identify potential features and apply appropriate ML techniques to forecast the best and worst-performing stocks over the next three months. My approach will ensure we can back-test our ML models and validate our findings.
Moving forward to Steps 2 and 3, I will analyze feature importance using methods such as Shapley Values and LIME while creating a dynamic dashboard to visualize these changes over time. In Step 4, I will identify the top 50 predicted winners for the portfolio and evaluate its performance against market benchmarks.
What specific features do you envision being pivotal in predicting future stock performance?
Thanks,
Muhammad
Hi Woloson . Awesome!
Your project is very similar to my last project. So I have rich experiences on Statistical Analysis, Excel, Mathematics, Financial Analysis and Statistics. I 'd like to share my experience with you.
Thanks, Aswin
I'll architect this solution using Python with Scikit-learn, TensorFlow, and Pandas for data manipulation, focusing on feature selection, model training, and validation through cross-validation techniques. My implementation will guarantee high-quality, reproducible results through rigorous EDA and robust model testing, delivering a predictive ML-driven stock picking strategy.
Key Points:
1. Implement comprehensive EDA to understand variable distributions and correlations.
2.
Hello Luke S. Hope you are doing well!
This is Efan , I checked your project detail carefully.
I am pretty much experienced with Mathematics, Excel, Statistical Analysis, Financial Analysis and Statistics for over 8 years, I can update you shortly.
Cheers
Efan
As mentioned in my profile, I’m a skilled software engineer with strong background.
I’m confident I can complete your task efficiently and to a high standard. Looking forward to hearing from you soon!
Hello,
In the competitive world of financial investment, staying ahead with innovative strategies is crucial. With the increasing demand for AI-driven stock investment products, it is imperative to leverage historical data patterns to predict high-performing portfolios. By organizing and preparing a large historical dataset, we can delve into the realm of machine learning algorithms to forecast the future winners in the stock market.
By conducting a thorough Exploratory Data Analysis and selecting key variables for ML algorithms, we aim to create a predictive model that can identify the best-performing stocks over the next 3-month period. Through back-testing and model evaluation, we can demonstrate the effectiveness of the ML-driven stock picking strategy.
Let's collaborate to unlock the potential of AI in stock investment strategies. Your satisfaction with the results is our priority, and payment is only required once you are fully content with the work. Please feel free to initiate the chat to discuss further details and kickstart this exciting project.
Sincerely,
Nimra H.
