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Portfolio Optimization

Updated: Sep 13, 2023


4 Words - Maximize Returns and Minimize Risk


Achieving the perfect balance between maximizing returns and minimizing risks in an investment portfolio can be likened to a delicate art. Diversifying assets across various investment avenues, such as stocks, bonds, mutual funds, etc., is the best way to spread out assets to maintain a risk-to-reward ratio.


Modern advancement in AI and Machine learning are used to automatically adjust investments, extract patterns from vast amounts of datasets. With thousands of factors applied, the computer can tirelessly dig into data and compare patterns one by one. With the help of complex mathematical models and neural networks, machine learning can quickly extract, transform, and load data to the database, applying complex algorithms and seeking the data signal until reaching the specific goal. Machine learning has the capability of uncovering valuable patterns that surpass the capabilities of traditional mathematical methods. Machine learning's knack for building non-linear relationships and reducing dimensionality significantly enhances portfolio optimization, unraveling the complex dynamics between risk and return.


In today's dynamic financial landscape, the fusion of Artificial Intelligence (AI) and Machine Learning (ML) has brought a transformative edge to portfolio optimization that would be unfeasible through traditional approaches. Much more interesting and exciting is that overtime, machine learning continues to self-learn, enhancing its capabilities and evolving its strategies.



How AI/ML helps?


Traditionally, portfolio optimization relied on quantitative analysis and hedging mechanisms. The advent of AI and machine learning has elevated this process by:


First, Process Extensive Data: Machine Learning can process a huge amount of data and extracting valuable patterns from the data, which were previously elusive using the traditional mathematical approach.

Second, Non Linear Relationship: Machine Learning can easily build a non-linear relationship and reduce dimensionality (a feat not easily achievable through other means) enhancing portfolio optimization.

Third, Complex Risk-Return Dynamics: Understanding the inttricate relationship between risk and return, which could end up multitude of factors that can be processed and identified inside a machine learning algorithm.


In the end, reinforcement learning can make the machine learn and continuously improve that no human being can beat.



Enhancing Corporate ROI


Companies should use machine learning to improve many different aspects, such as their spending, revenue, spend pattern, inventory, portfolios, etc. We use Statistical Analysis patterns to scrutinize the current portfolios, unearthing ways to continually improve. Machine learning also can analyze the actual risk.



Stock Recommendations


With capitalization on data, the machine will continuously mine and search patterns to find logical patterns that are consistently providing reasonable returns. It will try to pick from a time horizon either using historical data or based on randomized probability to determine the viability of stock to be recommended — which could be dramatically different from what we could normally see. Since machine learning works purely based on data output, it can spot the nuances a human might miss.



Parameters for Stock ROI


Machine learning and AI can recommend stock ROI based on following criterias (AKA. Parameters)


  • Company Strength

  • Industry leadership and position

  • Performance (historical)

  • Stock splits and other important information like ownership details



Profitability through AI in Portfolio Management


There are two primary algorithms for this (aside from deeper algorithms on top of common), historical and simulated. With this two algorithms we can look at the probability of profitability as well as risks;

  1. We can allocate capital weight by the certainty of prediction.

  2. We can allocate risk through risk prediction instead of by the capital.

  3. We can build hedging mechanisms by prediction.

  4. We can monitor thousands of signals simultaneously.



Essential Data Inputs


AI and machine learning models can thrive properly when high-quality data is available. Synthetics are not a common trend to help train the model, but in order to produce a high value and good precision and recall for your model, we need the data. Here are most commonly used:


  1. Market data

  2. Position data

  3. Execution prices

  4. Market Factors

  5. Beta/Alpha

  6. Risk and exposure data

  7. Company EPS, Income/Expenses etc.


Current Market Trends (US Markets)


We the advent of ChatGPT companies are looking to capitalize on the AI revolution. Data like this historical pattern provides the market direction that companies right now capitalize on and provide future guidance on what basket of stocks (or ETF) to choose for optimal investment for their investors, your company should follow through.


As you can see, if a person invested in the 2000s and allocated their portfolio that time, the ROI would have been great!



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