Stock Portfolio Management in the Presence of Downtrends Using Computational Intelligence
Abstract
:1. Introduction
2. Background
2.1. Fundamental Analysis
2.2. Artificial Neural Networks
2.3. MultiObjective Optimization Problem
2.4. Evolutionary MultiObjective Optimization
3. Literature Review
3.1. Portfolio Management: Price Forecasting, Stock Selection and Portfolio Optimization
3.2. Exploiting Uptrends and Downtrends in Strategies for Stock Investment
4. Methods and Materials
4.1. An Artificial Neural Network to Estimate Future Prices
 Close price. Last transacted price of the stock before the market officially closes.
 Open Price. First price of the stock at which it was traded at the open of the period’s trading.
 High. Highest price of the stock in the period’s trading.
 Low. Lowest price of the stock in the period’s trading.
 Average Price. Average price of the stock in the period’s trading.
 Market Capitalization. Price per share multiplied by the number of outstanding shares of a publicly held company.
 Return Rate. Profit on an investment over a period, expressed as a proportion of the original investment.
 Volume. Number of shares traded (or their equivalent in money) of a stock in a given period.
 Total asset turnover. Net sales over the average value of total assets on the company’s balance sheet between the beginning and the end of the period.
 Fixed asset turnover. Net sales over the average value of fixed assets.
 Volatility. Standard deviation of prices.
 General Capital. Number of preferred and common shares that a company is authorized to issue.
 Price to Earnings. Market value per share over earnings per share.
 Price to Book. Market price per share over book value per share.
 Price to Sales. Market price per share over revenue per share.
 Price to Cash Flow. Market price per share over operating cash flow per share.
4.2. Evolutionary Algorithms to Select Stocks
Algorithm 1 Differential evolution used to address Problem (3). 

 Forecasted return: Output of the ANN.
 Return on equity: Net income over average shareholder’s equity.
 Return on asset: Net income over total assets.
 Operating income margin: Operating earnings over revenue.
 Net income margin: Total liabilities over total shareholder’s equity.
 Levered free cash flow: Amount of money the company left over after paying its financial debts.
 Current ratio: Current assets over current liabilities.
 Quick ratio: (Cash and equivalents + marketable securities + accounts receivable) over current liabilities.
 Inventory turnover ratio: Net sales over ending inventory.
 Receivable turnover ratio: Net credit sales over average accounts receivable.
 Operating income growth rate: (Operating income in the current quarter − operating income at the previous quarter) over operating income in the previous quarter.
 Net income growth rate: (Net income after tax in the current quarter − net income after tax at the previous quarter) over net income after tax in the previous quarter.
4.3. Optimizing Stock Portfolios
5. Experiments
5.1. Experimental Design
5.2. Benchmarks
5.3. Parameter Setting
5.4. Results
6. Conclusions
 I
 A deeper study of the forecasting stage to test the performance of several AI methods by employing more data or different financial variables;
 II
 A deeper study on the selection stage to evaluate the performance of the system by employing different financial variables to build the stock portfolio;
 III
 A deeper study of the performance of the system by modifying different parameters in the optimization stage and comparing the results with other approaches;
 IV
 New experiments to show the robustness of the approach regarding (i) the number and type of alternatives in the universe of stocks, (ii) the number of selected stocks and (iii) the parameter values.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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S&P500 Index  Yang et al. (2019)  Solares et al. (2019)  Without Negative Trends  With Negative Trends  

Nov. 2018  1.75%  1.01%  1.87%  −5.11%  −4.87% 
Dec. 2018  −10.11%  −9.18%  −8.81%  −9.56%  −9.14% 
Jan. 2019  7.29%  10.88%  6.71%  6.77%  9.00% 
Feb. 2019  2.89%  7.47%  4.19%  7.00%  6.52% 
Mar. 2019  1.76%  0.20%  2.17%  0.89%  0.81% 
Apr. 2019  3.78%  4.29%  4.65%  3.88%  4.06% 
May. 2019  −7.04%  −7.22%  −5.65%  −7.66%  −5.77% 
Jun. 2019  6.45%  8.45%  7.53%  8.06%  9.33% 
Jul. 2019  1.30%  0.25%  0.92%  2.66%  2.64% 
Aug. 2019  −1.84%  −1.08%  −1.78%  −0.03%  −3.19% 
Sep. 2019  1.69%  −1.63%  0.83%  −6.20%  −4.96% 
Oct. 2019  2.00%  3.12%  1.67%  5.85%  5.09% 
Nov. 2019  3.29%  2.58%  4.00%  4.17%  5.43% 
Dec. 2019  2.78%  1.13%  2.39%  0.13%  0.36% 
Jan. 2020  −0.16%  0.81%  1.67%  2.13%  1.29% 
Feb. 2020  −9.18%  −9.09%  −9.28%  −7.22%  −4.96% 
Mar. 2020  −14.30%  −10.27%  −14.03%  −6.59%  −4.94% 
Apr. 2020  11.26%  14.33%  12.53%  19.64%  20.02% 
May. 2020  4.33%  7.09%  7.02%  11.54%  11.11% 
Jun. 2020  1.81%  −0.29%  0.15%  1.95%  2.88% 
Jul. 2020  5.22%  4.18%  5.87%  5.28%  10.65% 
Aug. 2020  6.55%  4.68%  3.90%  4.18%  5.94% 
Sep. 2020  −4.08%  −3.95%  −1.10%  −3.20%  −3.04% 
Oct. 2020  −2.85%  −4.74%  −2.05%  −5.88%  −2.31% 
Nov. 2020  9.71%  11.50%  11.91%  8.28%  4.97% 
Dec. 2020  3.58%  2.95%  5.39%  3.33%  4.37% 
Jan. 2021  −1.13%  −2.23%  −0.53%  −3.06%  −3.76% 
Feb. 2021  2.54%  3.43%  8.35%  1.51%  5.23% 
Mar. 2021  4.07%  7.22%  3.23%  0.88%  3.75% 
Apr. 2021  4.98%  6.05%  5.09%  6.00%  6.84% 
Average  1.28%  1.73%  1.96%  1.65%  2.45% 
Std desv.  5.61%  6.06%  5.76%  6.35%  6.27% 
Sum of Returns  Cumulative Returns  

S&P500 Index  Yang et al. (2019)  Solares et al. (2019)  Without Down Trends  With Down Trends  S&P500 Index  Yang et al. (2019)  Solares et al. (2019)  Without Down Trends  With Down Trends  
Nov. 2018  1.75%  1.01%  1.87%  −5.11%  −4.87%  1.75%  1.01%  1.87%  −5.11%  −4.87% 
Dec. 2018  −8.35%  −8.17%  −6.94%  −14.67%  −14.01%  −8.53%  −8.27%  −7.11%  −14.18%  −13.56% 
Jan. 2019  −1.06%  2.70%  −0.24%  −7.89%  −5.01%  −1.86%  1.71%  −0.88%  −8.37%  −5.79% 
Feb. 2019  1.83%  10.17%  3.95%  −0.89%  1.51%  0.98%  9.31%  3.28%  −1.95%  0.36% 
Mar. 2019  3.59%  10.37%  6.12%  0.00%  2.32%  2.76%  9.53%  5.52%  −1.08%  1.17% 
Apr. 2019  7.37%  14.66%  10.77%  3.88%  6.38%  6.64%  14.23%  10.42%  2.76%  5.28% 
May. 2019  0.33%  7.44%  5.12%  −3.78%  0.61%  −0.87%  5.98%  4.18%  −5.11%  −0.80% 
Jun. 2019  6.78%  15.89%  12.65%  4.28%  9.94%  5.53%  14.93%  12.03%  2.54%  8.46% 
Jul. 2019  8.08%  16.14%  13.58%  6.94%  12.58%  6.89%  15.22%  13.07%  5.27%  11.32% 
Aug. 2019  6.24%  15.06%  11.80%  6.91%  9.39%  4.93%  13.97%  11.05%  5.23%  7.77% 
Sep. 2019  7.92%  13.43%  12.63%  0.70%  4.43%  6.70%  12.11%  11.98%  −1.29%  2.43% 
Oct. 2019  9.93%  16.54%  14.30%  6.56%  9.52%  8.83%  15.61%  13.85%  4.48%  7.64% 
Nov. 2019  13.22%  19.12%  18.30%  10.72%  14.95%  12.42%  18.59%  18.40%  8.84%  13.48% 
Dec. 2019  16.00%  20.25%  20.68%  10.85%  15.31%  15.54%  19.93%  21.22%  8.97%  13.89% 
Jan. 2020  15.84%  21.06%  22.36%  12.98%  16.60%  15.35%  20.90%  23.25%  11.30%  15.36% 
Feb. 2020  6.65%  11.98%  13.07%  5.76%  11.64%  4.76%  9.92%  11.81%  3.26%  9.64% 
Mar. 2020  −7.65%  1.71%  −0.96%  −0.83%  6.70%  −10.22%  −1.37%  −3.88%  −3.54%  4.23% 
Apr. 2020  3.61%  16.04%  11.57%  18.81%  26.73%  −0.12%  12.77%  8.16%  15.40%  25.10% 
May. 2020  7.94%  23.13%  18.58%  30.36%  37.84%  4.21%  20.76%  15.75%  28.72%  38.99% 
Jun. 2020  9.75%  22.84%  18.73%  32.31%  40.72%  6.09%  20.41%  15.91%  31.24%  43.00% 
Jul. 2020  14.97%  27.01%  24.60%  37.59%  51.37%  11.63%  25.43%  22.72%  38.16%  58.23% 
Aug. 2020  21.52%  31.69%  28.51%  41.77%  57.31%  18.94%  31.30%  27.51%  43.94%  67.63% 
Sep. 2020  17.43%  27.74%  27.41%  38.57%  54.28%  14.09%  26.12%  26.11%  39.33%  62.54% 
Oct. 2020  14.59%  23.01%  25.36%  32.68%  51.97%  10.84%  20.14%  23.53%  31.13%  58.80% 
Nov. 2020  24.30%  34.51%  37.27%  40.97%  56.94%  21.60%  33.97%  38.24%  41.99%  66.69% 
Dec. 2020  27.88%  37.47%  42.66%  44.30%  61.31%  25.96%  37.92%  45.70%  46.73%  73.97% 
Jan. 2021  26.75%  35.23%  42.14%  41.24%  57.55%  24.54%  34.85%  44.93%  42.24%  67.43% 
Feb. 2021  29.29%  38.66%  50.48%  42.75%  62.78%  27.70%  39.47%  57.03%  44.39%  76.18% 
Mar. 2021  33.36%  45.88%  53.71%  43.63%  66.52%  32.90%  49.54%  62.10%  45.66%  82.78% 
Apr. 2021  38.35%  51.93%  58.80%  49.64%  73.36%  39.52%  58.59%  70.35%  54.41%  95.28% 
Sharpe Ratio  Sortino Ratio  

S&P’s 500  0.1831  0.2529 
Yang et al. (2019)  0.2445  0.4072 
Solares et al. (2019)  0.2966  0.4551 
Without downtrends  0.2213  0.3884 
With downtrends  0.3502  0.7223 
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Díaz, R.; Solares, E.; deLeónGómez, V.; Salas, F.G. Stock Portfolio Management in the Presence of Downtrends Using Computational Intelligence. Appl. Sci. 2022, 12, 4067. https://doi.org/10.3390/app12084067
Díaz R, Solares E, deLeónGómez V, Salas FG. Stock Portfolio Management in the Presence of Downtrends Using Computational Intelligence. Applied Sciences. 2022; 12(8):4067. https://doi.org/10.3390/app12084067
Chicago/Turabian StyleDíaz, Raymundo, Efrain Solares, Victor deLeónGómez, and Francisco G. Salas. 2022. "Stock Portfolio Management in the Presence of Downtrends Using Computational Intelligence" Applied Sciences 12, no. 8: 4067. https://doi.org/10.3390/app12084067