Artificial intelligence (AI) algorithms have the potential to revolutionize the way alternative investments are selected and managed. By leveraging large sets of data and complex mathematical models, AI algorithms can identify patterns and make investment recommendations that are not immediately obvious to human investors.  AI in alternative investment portfolios promises to be a significant area of growth and opportunity.

Alternative asset portfolios can leverage artificial intelligence in many ways. This study focuses on the use of AI in more liquid, volatile assets like currencies, digital currencies and commodities.  Particularly, it focuses on the following key areas of potential artificial advantage:

• Data analysis
• Predictive modeling 
• Risk management 
• Asset selection
• Portfolio optimization 
• Automated trading

1. Data analysis: For commodities, AI can analyze data from various sources such as weather patterns, crop yields, and global supply and demand to identify trends that could affect prices. For currencies, AI can analyze economic indicators such as inflation rates and interest rates to identify potential exchange rate movements. For digital currencies, AI can analyze historical trading data to identify patterns and predict future price movements.
   1. Further reading is available for 
      • Commodities: “Artificial Intelligence and Commodities: Benefits and Challenges” by Ramiah, Ramayah, and Tang (2021) in Journal of Risk and Financial Management.
      • Currencies: “The Application of Artificial Intelligence in Foreign Exchange Rate Forecasting” by Zhang and Wang (2019) in Journal of Financial Risk Management.
      • Digital currencies: “Forecasting Cryptocurrency Prices with Deep Learning: A Long-term Convolutional Neural Network Approach” by Zhang et al. (2019) in IEEE Transactions on Neural Networks and Learning Systems.
2. Predictive modeling: For commodities, AI can create models that predict prices based on factors such as supply and demand, weather patterns, and geopolitical events. For currencies, AI can create models that predict exchange rate movements based on economic indicators, political events, and central bank policies. For digital currencies, AI can create models that predict price movements based on factors such as trading volume, investor sentiment, and blockchain activity.
   1. Further reading available for

• Commodities: “A Machine Learning Approach to Predicting Commodity Prices” by Rehman et al. (2019) in Journal of Forecasting.
• Currencies: “Predicting Exchange Rates Using Machine Learning Algorithms: The Case of the Euro/US Dollar” by Al-Zoubi and Al-Kassasbeh (2019) in Journal of Risk and Financial Management.
• Digital currencies: “Deep Learning for Cryptocurrency Price Prediction: An Empirical Study” by Xiong et al. (2018) in IEEE Access.

1. Risk management: For commodities, AI can identify potential risks such as supply chain disruptions or geopolitical events that could affect prices. For currencies, AI can identify potential risks such as political instability or changes in central bank policies that could affect exchange rates. For digital currencies, AI can identify potential risks such as cyberattacks or regulatory changes that could affect prices. AI can also be used to identify and mitigate the risk inherent in alternative investments.
   1. Further reading available for
      • For commodities: “A Review of Commodity Risk Management with Reference to Energy” by Odularu and Adeyemo (2019) in African Journal of Economic and Management Studies.
      • For currencies: “Risk Management in Foreign Exchange Markets with Machine Learning Algorithms” by Skarpetis et al. (2019) in Journal of Financial Risk Management.
      • For digital currencies: “Risk Management of Cryptocurrencies: A Review” by Tseng and Chen (2019) in Journal of Risk and Financial Management.
2. Asset selection: One way to leverage AI algorithms for building a portfolio of alternative investments is to use them for asset selection. For example, a machine learning algorithm could be trained on historical financial data to identify patterns that are indicative of future performance. Once the algorithm has been trained, it can be used to evaluate new investments and identify those that are likely to perform well. This can be especially useful for identifying undervalued assets that may have been overlooked by human investors. In modeling different assets, their historical data, returns and behaviors, asset managers can get a better view as to the opportunities and constraints in the broad world of asset management.
3. Portfolio optimization: Use AI algorithms for portfolio construction and management. A portfolio optimization algorithm can be used to determine the optimal mix of assets based on factors such as risk, return, and correlation. Additionally, a reinforcement learning algorithm could be used to continuously monitor and rebalance the portfolio based on changing market conditions. For commodities, AI can analyze historical data and identify correlations between different commodities to help investors diversify their portfolios and maximize returns. For currencies, AI can help investors balance their portfolios by identifying the most profitable currency pairs to trade. For digital currencies, AI can help investors optimize their portfolios by identifying the most promising cryptocurrencies based on their historical performance and other factors.
   1. Further reading available for
      • For commodities: “Optimizing Commodities Portfolio Allocation with Machine Learning Techniques” by Wang et al. (2020) in Journal of Intelligent Manufacturing.
      • For currencies: “Foreign Exchange Portfolio Optimization: A Study on Artificial Intelligence and Optimization Techniques” by Samadi et al. (2020) in Journal of Financial Markets, Institutions and Instruments.
      • For digital currencies: “A Multi-Objective Optimization Framework for Cryptocurrency Portfolio Management” by Li et al. (2019) in IEEE Transactions on Cybernetics.
4. Automated trading: For commodities, AI can be used to automate trading strategies based on pre-defined rules such as buying or selling when prices reach certain levels. For currencies, AI can be used to automate trading strategies based on technical indicators such as moving averages or stochastic oscillators. For digital currencies, AI can be used to automate trading strategies based on algorithms that analyze market data in real-time to identify profitable trading opportunities.
   1. Further reading available for 
      • Algorithms” by Kamble and Jain (2019) in Journal of Computational Science.
      • For currencies: “Automated Forex Trading System Based on Machine Learning Algorithms” by Tompkins and Hasan (2019) in Journal of Financial Risk Management.
      • For digital currencies: “Deep Reinforcement Learning-based Cryptocurrency Trading Strategy” by Hu et al. (2019) in IEEE Access.

Artificial intelligence is only now in its infancy. While the promise of AI in investing in alternative asset classes is high, there are limitations in using any new approach. For example, AI models are only as good as the data they are trained on, and they may be susceptible to overfitting or other forms of bias if the data is not properly cleaned or if the model is not properly validated. Therefore, it’s crucial to be cautious and critical when using AI algorithms for investment decisions.

One more thing to keep in mind is, the widespread adoption of AI in finance is relatively new and it’s still ongoing. But, with the right approach and the right data, the use of AI algorithms can help investors to make more informed decisions and achieve better returns.