Preprint

Article

Withdrawn:

Enhancing Financial Intelligence: AI Robo-Advisors for Strategic Investment Decisions

Altmetrics

Downloads

Views

138

Comments

This preprint has been withdrawn

Jeyadev Needhi^*

,Ram Prasath G,Mohamed Riffath K,Manokar S

Jeyadev Needhi^*

,Ram Prasath G,Mohamed Riffath K,Manokar S

This version is not peer-reviewed

Submitted:

17 June 2024

Posted:

18 June 2024

Withdrawn:

06 August 2024

Alerts

Abstract

The increasing popularity of Robo-Advisors highlights the need for sophisticated systems combining big data analytics and deep learning for portfolio optimization. Despite their potential, these AI-driven platforms face notable challenges such as financial data inaccuracies and the risk of overfitting in deep learning models. Overcoming these hurdles requires a concerted effort to enhance reliability and performance. To tackle these challenges effectively, it is imperative to prioritize real-time data integration while minimizing reliance on historical data. By adopting this approach, Robo-Advisors can better adapt to dynamic market conditions, enabling them to provide informed and timely investment recommendations. This proactive stance not only ensures computational efficiency but also fosters investor confidence in the platform's ability to navigate evolving market dynamics. In conclusion, by continually refining data integration methods and reducing dependencies on historical data, Robo-Advisors can offer more accurate and timely recommendations. This not only enhances the overall investor experience but also upholds ethical standards in automated investment platforms.

Keywords:

Subject: Computer Science and Mathematics - Computer Science

1. Introduction

Robo-Advisors have revolutionized investment management, leveraging the power of machine learning algorithms and big data analytics to construct and optimize investment portfolios autonomously. These platforms promise accessibility, efficiency, and potentially superior returns for investors, democratizing financial advisory services traditionally accessible only to affluent individuals. However, amidst their growing popularity, Robo-Advisors encounter formidable challenges that necessitate careful consideration and innovative solutions to ensure their effectiveness and reliability in navigating the complexities of financial markets.

The evolution of Robo-Advisors is deeply intertwined with advancements in artificial intelligence (AI) and data science. By harnessing vast amounts of data and sophisticated algorithms, these platforms can provide personalized investment strategies that align with individual investor profiles, risk appetites, and financial goals. Yet, despite these advancements, the accuracy and reliability of AI-driven investment recommendations remain contingent on the quality and timeliness of the data used.

A significant challenge facing Robo-Advisors is the integration of real-time data. Financial markets are dynamic and influenced by a myriad of factors, including economic indicators, geopolitical events, and market sentiment. Relying solely on historical data can limit a Robo-Advisor’s ability to capture timely market opportunities and respond promptly to fluctuations. Therefore, prioritizing real-time data integration is critical for enhancing the responsiveness and agility of these platforms.

Overfitting in deep learning models is another critical issue that can undermine the effectiveness of Robo-Advisors. Overfitting occurs when a model learns the noise in the training data rather than the underlying patterns, leading to poor generalization on unseen data. This can result in suboptimal investment strategies and increased risk for investors. To mitigate this risk, it is essential to employ regularization methods and ensemble learning techniques that enhance model generalization and adaptability to dynamic market conditions.

Furthermore, transparency and interpretability of AI models used in Robo-Advisors are paramount for building investor trust. Investors need to understand the rationale behind the recommendations provided by these platforms. Enhancing model interpretability through visualization techniques and developing interpretable models can help bridge this gap, ensuring that investors are well-informed about the decision-making processes of their Robo-Advisors.

Ethical considerations also play a crucial role in the development and deployment of Robo-Advisors. Ensuring regulatory compliance and adhering to ethical standards are vital for maintaining investor confidence and mitigating legal risks. This includes integrating regulatory compliance frameworks into the platform and conducting regular audits to uphold ethical standards in automated investment platforms.

Promoting financial inclusion is another significant objective for Robo-Advisors. By catering to diverse investor profiles and addressing accessibility barriers, these platforms can empower a broader range of individuals to participate in financial markets. Developing inclusive user interfaces and personalized investment strategies can help achieve this goal, making financial advisory services more accessible to all.

Investor education is also essential for fostering informed decision-making and building trust in Robo-Advisors. Providing comprehensive educational resources and guidance about the principles and mechanics of automated investment platforms can empower investors to make sound financial decisions aligned with their goals and risk tolerance.

The objective of this study is to address these challenges by advancing model interpretability, ensuring regulatory compliance, promoting financial inclusion, and enhancing investor education. By continually refining data integration methods and reducing dependencies on historical data, Robo-Advisors can offer more accurate and timely recommendations, ultimately enhancing the overall investor experience.

While Robo-Advisors hold significant promise in revolutionizing investment management through AI and big data analytics, addressing the challenges of data accuracy, model overfitting, transparency, ethical standards, financial inclusion, and investor education is imperative. This study aims to explore and propose solutions to these challenges, paving the way for more reliable, effective, and inclusive AI-driven investment platforms.

2. Related Work

Barua and Sharma (2022) introduce dynamic Black-Litterman portfolios with views derived from CNN-BiLSTM predictions [1]. Their study utilizes a CNN-BiLSTM model to predict MSCI Asia Pacific sector indices, integrating these predictions into the Black-Litterman framework for portfolio optimization. However, the research is limited to the MSCI Asia Pacific sector and lacks comparisons with other prediction models.

Bukhari et al. (2020) present the Fractional Neuro-Sequential ARFIMA-LSTM model for financial market forecasting [2]. This model integrates ARFIMA and LSTM techniques to enhance prediction accuracy. However, empirical validation is necessary to ensure practical application. Chandrika et al. (2020) investigate Hidden Markov Models (HMM) combined with ARIMA for stock market forecasting [3]. The study raises concerns about the oversimplification of stock market dynamics and limited parameter explanations.

Hen and Zho (2021) propose a stock prediction model combining Genetic Algorithm (GA) feature selection with LSTM neural networks [4]. The model’s effectiveness depends on GA feature selection quality and parameter tuning, requiring further research. Chacón, Kesici, and Najafirad (2020) integrate ensemble empirical mode decomposition with RNNs to improve financial time series prediction accuracy [5]. The method’s generalizability to other markets needs further investigation.

Gumelar et al. (2020) employ LSTM and XGBoost for stock market prediction [6]. The lack of detailed discussion on feature selection and preprocessing may impact model robustness. Liang and Yang (2022) introduce the FF-HMM method for stock prediction, utilizing stochastic price fluctuations [7]. Further research is needed to assess scalability and applicability to different datasets. Lu et al. (2020) combine CNN, BiLSTM, and attention mechanism (AM) for intraday stock price prediction [8]. Challenges such as model complexity and overfitting require further research.

Ma, Han, and Wang (2020) use Deep Neural Networks (DNNs) for stock return prediction and portfolio optimization [9]. The study lacks comparison with traditional methods and relies on a single dataset. Singh et al. (2023) present the CNN-LSTM+MV model for stock selection and portfolio optimization [10]. The study highlights challenges in hyperparameter selection and model interpretation. Sun et al. (2023) introduce the TVC-BL model for optimal asset allocation, combining time-varying covariance estimation with the Black-Litterman approach [11]. The study acknowledges limitations such as a short context window and dependence on subjective investor views.

Wiharno et al. (2023) integrate investor views into the Black-Litterman model for portfolio optimization in different market conditions [12]. The study’s focus on the Indonesian market from 2011 to 2021 and assumptions of normality limit generalizability. Wutsqa et al. (2021) combine Elman Recurrent Neural Network (ERNN) with the Black-Litterman Model for portfolio optimization [13]. The study lacks empirical validation and comparison. Yadav, Jha, and Sharan (2020) develop an LSTM model optimized for the Indian stock market [14]. The study’s narrow comparison limits insights into the LSTM model’s performance.

Yu et al. (2023) propose ML-GATA for stock prediction using a Multilevel Graph Attention Model [15]. The approach integrates BERT with graph neural network-based techniques, demonstrating improved performance over benchmark models. In summary, these studies highlight various approaches to enhancing financial time series predictions and portfolio optimization, each with unique methodologies and limitations. The insights gained from these works inform the proposed research direction.

3. Proposed Work

The proposed system integrates dataset collection, preprocessing, deep learning model development, and data visualization. Figure 1 illustrates the architecture diagram of the system.

3.1. Dataset Collection and Preprocessing

The dataset collection module automates financial data retrieval using the Yahoo Finance API. The preprocessing module converts data into a supervised learning format, calculates logarithmic returns, and reshapes data for LSTM compatibility.

Algorithm 1: Dataset Collection Module

1:: Initialize a list of symbols for ETFs.
2:: Define the date range for data collection.
3:: for each symbol in the list do
4:: Fetch historical data using Yahoo Finance API.
5:: Save the data to a CSV file.
6:: end for
7:: Reorder columns in CSV to place ’Symbol’ at the beginning.

Let

P_{t}

denote the price of the asset at time t. The logarithmic return

R_{t}

is calculated as:

R_{t} = ln (\frac{P_{t}}{P_{t - 1}})

(1)

3.2. LSTM Model Development

The LSTM model is trained to discern patterns in historical data, employing regularization and early stopping to prevent overfitting. A learning rate scheduler dynamically adjusts the learning rate during training.

Algorithm 2: LSTM Training Module

1:: Preprocess the data to create input and target sequences.
2:: Split data into training and testing sets.
3:: Define the LSTM model architecture with regularization layers.
4:: Compile the model with mean squared error loss and Adam optimizer.
5:: Train the model with early stopping and learning rate scheduler.
6:: Evaluate model performance on testing set.

The LSTM model is trained to minimize the mean squared error (MSE):

MSE = \frac{1}{n} \sum_{i = 1}^{n} {(y_{i} - {\hat{y}}_{i})}^{2}

(2)

where

y_{i}

are the actual values and

{\hat{y}}_{i}

are the predicted values.

3.3. Portfolio Optimization

The Black-Litterman model integrates investor views with market equilibrium to optimize portfolio allocations. The optimization process considers historical mean returns, covariance matrices, and investor perspectives.

Algorithm 3: Black-Litterman Portfolio Optimization

1:: Collect historical mean returns and covariance matrix.
2:: Define investor views and confidence levels.
3:: Calculate the implied equilibrium returns.
4:: Integrate views into the Black-Litterman model to adjust the equilibrium returns.
5:: Optimize the portfolio using the adjusted returns and covariance matrix.

Let

Π

denote the implied equilibrium returns,

Σ

the covariance matrix, Q the investor views, and

Ω

the confidence matrix. The adjusted returns

μ

are given by:

μ = Π + τ Σ P^{T} {(Ω + τ P Σ P^{T})}^{- 1} (Q - P Π)

(3)

where

τ

is a scaling factor and P is the pick matrix.

Algorithm 4: Data Visualization Module

1:: Load the optimized portfolio data.
2:: Plot daily returns, cumulative returns, and volatility.
3:: Visualize the portfolio weights and performance metrics.
4:: Generate interactive charts for investor analysis.

3.4. Data Visualization & Black-Litterman Optimization

Data visualization plays a critical role in interpreting the results of the portfolio optimization and understanding market trends. The system uses various visualization techniques to present the data clearly and effectively.

Daily returns

R_{t}

and cumulative returns

C R_{t}

are calculated as:

R_{t} = \frac{P_{t} - P_{t - 1}}{P_{t - 1}}

(4)

C R_{t} = \prod_{i = 1}^{t} (1 + R_{i}) - 1

(5)

The process begins with data acquisition, collecting both asset prices and LSTM forecasting results. Using these forecasts, investor views are formulated to shape expectations regarding asset performance. Historical metrics are computed next, including historical mean returns and the covariance matrix for risk assessment. Investor perspectives are then incorporated by adjusting historical mean returns based on views and updating the covariance matrix to reflect confidence. Employing the Black-Litterman model and Markowitz Mean-Variance Optimization, portfolio optimization is conducted to determine optimal weights, maximizing expected return or minimizing risk based on investor preferences.

4. Results and Analysis

Performance analysis involved daily returns, linear regression, and portfolio optimization. Table 1 compares the benchmark and optimized portfolios.

The optimized portfolio outperformed the initial portfolio with a cumulative return of 21.34%, demonstrating the efficacy of the Black-Litterman model in enhancing risk-adjusted returns.

The initial portfolio is constructed under the premise of equally distributing the principal investment across six ETFs: THLV, GDIV, GYLD, DRLL, CGV, and HGER. This allocation generates a cumulative return of approximately 15.91%. Subsequently, the portfolio undergoes optimization, focusing on three ETFs: GDIV, GYLD, and HGER, with respective investment proportions of 77.772%, 9.259%, and 13.014% of the total amount. This refined investment approach outperforms the original strategy of uniform allocation across all six ETFs. Facilitated by the Black-Litterman model, the optimized strategy yields a cumulative return of 21.34%, surpassing the returns of the initial portfolio. Furthermore, there is a notable increase in the Sharpe ratio from 0.89 to 1.04, indicative of enhanced risk-adjusted returns. This optimization also leads to augmented percentages of daily, monthly, and yearly returns.

4.1. Cumulative Return vs Benchmark

Figure 2 illustrates the cumulative returns of the optimized portfolio compared to the benchmark. The cumulative return

C R_{t}

is calculated as:

C R_{t} = \prod_{i = 1}^{t} (1 + R_{i}) - 1

(6)

where

R_{i}

is the daily return at time i.

4.2. Log-Scaled Comparison

Figure 3 shows the log-scaled cumulative returns of the optimized portfolio and the benchmark. Logarithmic scaling helps in visualizing the performance over time by reducing the impact of large changes.

4.3. Volatility Comparison

Figure 4 compares the annualized volatility of the optimized portfolio with the benchmark. The annualized volatility

σ

is calculated as:

σ = \sqrt{252} \times std (R_{t})

(7)

where

std (R_{t})

is the standard deviation of daily returns.

4.3.1. Worst 5 Drawdowns

Table 3 and Table 4 list the worst 5 drawdowns of the optimized portfolio. A drawdown is the peak-to-trough decline during a specific period, usually quoted as the percentage between the peak and the trough. The maximum drawdown is calculated as:

Max Drawdown = \frac{Trough Value - Peak Value}{Peak Value}

(8)

5. Conclusion and Future Works

In conclusion, the project successfully collected, preprocessed, and analyzed daily returns data for six stocks and the S&P 500 index, utilizing various tools and libraries such as `quantstats`, `pandas`, `matplotlib`, `seaborn`, and `plotly`. The linear regression analysis provided insights into the relationship between each stock’s returns and the market index, offering a basis for further portfolio optimization and risk management strategies. The data visualization techniques helped visualize the dataset’s characteristics and the performance of the ML model.

For future work, there is ample opportunity to explore more sophisticated avenues in both deep learning (DL) modeling and portfolio optimization techniques. Expanding the depth of DL models could lead to improved forecasting accuracy, allowing for the capture of complex and subtle patterns inherent in stock market data. Furthermore, incorporating a broader range of features beyond just the market index, such as macroeconomic indicators or sector-specific data, holds the potential to enhance the predictive power of these models. Moreover, implementing advanced portfolio optimization techniques, such as the Black-Litterman model, presents an avenue for further refining portfolio performance and risk management capabilities. By integrating more robust methodologies for asset allocation, investors can potentially achieve better-balanced portfolios that are more resilient to market fluctuations.

Additionally, the future work could extend the analysis to include a more comprehensive set of stock symbols, moving beyond the current limitation to only six symbols. This broader scope would provide a more holistic understanding of market dynamics and offer opportunities to optimize investment strategies across a wider spectrum of assets. Furthermore, incorporating sentiment analysis from news and social media could provide more timely and comprehensive insights into market trends and investor sentiment. Enhancing the visualization tools to provide interactive and dynamic visualizations could also improve the user experience and facilitate better decision-making. Overall, there is a wide range of possibilities for future enhancements and refinements to make the project more robust and effective in the field of financial analytics and investment decision-making.

References

Barua, R. and Sharma, A.K., (2022). Dynamic Black Litterman portfolios with views derived via CNN-BiLSTM predictions. Finance Research Letters, 49, p.103111.
Bukhari, A.H. , Raja, M.A.Z., Sulaiman, M., Islam, S., Shoaib, M. and Kumam, P., (2020). Fractional neuro-sequential ARFIMA-LSTM for financial market forecasting. IEEE Access, 8, pp.71326-71338.
Chandrika, P.V. , Visalakshmi, K. and Srinivasan, K.S., (2020), March. Application of Hidden Markov Models in Stock Trading. In 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS) (pp. 1144-1147). IEEE.
Chen, S. and Zhou, C., (2020). Stock prediction based on genetic algorithm feature selection and long short-term memory neural network. IEEE Access, 9, pp.9066-9072.
Chacón, H.D. , Kesici, E. and Najafirad, P., (2020). Improving financial time series prediction accuracy using ensemble empirical mode decomposition and recurrent neural networks. IEEE Access, 8, pp.117133-117145.
Gumelar, A.B. , Setyorini, H., Adi, D.P., Nilowardono, S., Widodo, A., Wibowo, A.T., Sulistyono, M.T. and Christine, E., (2020), September. Boosting the accuracy of stock market prediction using xgboost and long short-term memory. In 2020 International Seminar on Application for Technology of Information and Communication (iSemantic) (pp. 609-613). IEEE.
Liang, K. and Yang, S., (2022), August. FF-HMM: A Novel Fluctuation Fusion based Markov Model for Stock Prediction. In 2022 International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM) (pp. 85-92). IEEE.
Lu, W. , Li, J., Wang, J. and Qin, L., (2021). A CNN-BiLSTM-AM method for stock price prediction. Neural Computing and Applications, 33(10), pp.4741-4753.
Ma, Y. , Han, R. and Wang, W., (2020). Prediction-based portfolio optimization models using deep neural networks. IEEE Access, 8, pp.115393-115405.
Singh, P. , Jha, M., Sharaf, M., Elmeligy, M.A. and Gadekallu, T.R., (2023). Harnessing a Hybrid CNN-LSTM Model for Portfolio Performance: A Case Study on Stock Selection and Optimization. IEEE Access.
Sun, Y. , Wu, Y. and De, G., (2023). A Novel Black-Litterman Model with Time-Varying Covariance for Optimal Asset Allocation of Pension Funds. Mathematics, 11(6), p.1476.
Wiharno, H. , Lesmana, A.S., Maulana, Y. and Komarudin, M.N., (2023). STOCK PORTFOLIO OPTIMIZATION IN BULLISH AND BEARISH CONDITIONS USING THE BLACK-LITTERMAN MODEL. Jurnal Manajemen dan Kewirausahaan, 25(2), pp.92-104.
Wutsqa, D.U. , Pamungkas, M.A. and Subekti, R., (2021). Black-litterman model with views prediction using Elman recurrent neural network. Universal Journal of Accounting and Finance, 9(6), pp.1297-1311.
Yadav, A., Jha, C.K. and Sharan, A., (2020). Optimizing LSTM for time series prediction in Indian stock market. Procedia Computer Science, 167, pp.2091-2100.
Yu, Y., Fu, K., and Li, B., (2023). Multi-Feature Supervised Reinforcement Learning for Stock Trading. IEEE Access, 11, pp.77840-77855.

Figure 1. Architecture Diagram of the Proposed System

Figure 2. Cumulative Return vs Benchmark

Figure 3. Log-Scaled Comparison

Figure 4. Volatility Comparison

Table 1. Benchmark and Strategy Comparison

Metric	Benchmark	Strategy
Cumulative Return	15.91%	21.34%
Sharpe Ratio	0.89	1.04
Volatility (ann.)	11.18%	12.52%
Max Drawdown	-9.42%	-10.45%

Table 2. Detailed Performance Metrics

Metric	Benchmark	Strategy
Start Period	2022-10-03	2022-10-03
End Period	2024-05-03	2024-05-03
Risk-Free Rate	0.0%	0.0%
Time in Market	100.0%	100.0%
Cumulative Return	15.91%	21.34%
CAGR%	6.65%	8.8%
Sharpe	0.89	1.04
Prob. Sharpe Ratio	86.87%	90.62%
Smart Sharpe	0.88	1.03
Sortino	1.31	1.58
Metric	Benchmark	Strategy
Smart Sortino	1.3	1.57
Sortino/ $\sqrt{2}$	0.93	1.12
Smart Sortino/ $\sqrt{2}$	0.92	1.11
Omega	1.19	1.19
Max Drawdown	-9.42%	-10.45%
Longest DD Days	405	365
Volatility (ann.)	11.18%	12.52%
$R^{2}$	0.77	0.77
Information Ratio	0.03	0.03
Calmar	0.71	0.84
Skew	0.06	0.31
Kurtosis	1.1	2.55

Table 3. Worst 5 Drawdowns (Part 1)

Start	Valley	End
2022-12-01	2023-03-15	2023-11-30
2024-04-01	2024-04-18	2024-05-03
2022-10-06	2022-10-11	2022-10-24
2023-12-20	2024-01-17	2024-01-22
2022-10-31	2022-11-03	2022-11-07

Table 4. Worst 5 Drawdowns (Part 2)

Days	Max DD	99% Max DD
365	-10.45%	-9.74%
33	-4.76%	-4.58%
19	-4.06%	-3.88%
34	-2.17%	-2.01%
8	-2.12%	-1.92%

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.

MDPI Initiatives

Important Links

Choose an area of interest and we will send you notifications of new preprints at your preferred frequency.

Disclaimer

Withdrawn:

Enhancing Financial Intelligence: AI Robo-Advisors for Strategic Investment Decisions

Abstract

1. Introduction

2. Related Work

3. Proposed Work

3.1. Dataset Collection and Preprocessing

3.2. LSTM Model Development

3.3. Portfolio Optimization

3.4. Data Visualization & Black-Litterman Optimization

4. Results and Analysis

4.1. Cumulative Return vs Benchmark

4.2. Log-Scaled Comparison

4.3. Volatility Comparison

4.3.1. Worst 5 Drawdowns

5. Conclusion and Future Works

References

MDPI Initiatives

Important Links

Subscribe