Abstract
Nowadays, the challenges that covid-19 has generated to the financial community that operates within the stock market
has generated a greater uncertainty in the profitability and consequently has made this practice more difficult. To
overcome that problem the present study aims to develop a model that facilitates this work; this model uses the SVR
regression algorithm and through technical indicators provide us with the possible trend that the stock may take in the
future and thus suggest that the investor in question buys, sells or holds the stock in view of that result. As a result of
the project, it was proposed to use 7 technical indicators RSI, MACD, ROC, WMA, OBV, the Williams indicator and
the stochastic oscillator that determine the current market condition. After validating the model, it was concluded that
there are different Peruvian companies that have been able to overcome the difficulties of the pandemic with enough
growth potential during this post-covid period.
has generated a greater uncertainty in the profitability and consequently has made this practice more difficult. To
overcome that problem the present study aims to develop a model that facilitates this work; this model uses the SVR
regression algorithm and through technical indicators provide us with the possible trend that the stock may take in the
future and thus suggest that the investor in question buys, sells or holds the stock in view of that result. As a result of
the project, it was proposed to use 7 technical indicators RSI, MACD, ROC, WMA, OBV, the Williams indicator and
the stochastic oscillator that determine the current market condition. After validating the model, it was concluded that
there are different Peruvian companies that have been able to overcome the difficulties of the pandemic with enough
growth potential during this post-covid period.
| Original language | American English |
|---|---|
| Title of host publication | Prediction of Peruvian Companies' Stock Prices Using Machine Learning |
| Place of Publication | AUSTRALIA |
| Publisher | IEOM Society International |
| Chapter | 1 |
| Number of pages | 11 |
| ISBN (Electronic) | 979-8-3507-0542-3 |
| State | Published - 15 Mar 2023 |