Farida Titik Kristanti

Covid-19 has an impact on stock investment, especially in Indonesia, marked by the decline in the Jakarta Composite Index (JCI) at the beginning of the Covid-19 pandemic. During the Covid-19 era, there was a lot of negative information about the uncertainty of the market, which made investors irrational about the choice of stocks in the portfolio. So this research will have a hypothesis that the High Volatility stock group will be the best portfolio in Covid-19 conditions. The sample used is the Group of stocks that have the largest market capitalization value in JCI. Stocks with large market caps are chosen because of one of the indicators of blue chip stock. The sample will be divided into three portfolio groups, High Volatility, Moderate Volatility, and Low Volatility. The results obtained that High Volatility became the best portfolio during the Covid-19 period. The results of this study prove that the group of stocks with High Volatility will get positive returns and sharpe performance results are the highest and positive, compared to moderate volatility and low volatility portfolios. This result arises because stocks with High Volatility are subject to large price fluctuations and in this situation, investors can invest in these stocks in a short time frame. The short-term process is carried out regularly so that it can be in accordance with investors' expectations for investments in the portfolio.

Construction companies are very dependent on the projects carried out by a company. Therefore, measuring whether a company is distressed or non-distressed can be done by looking at the ratios derived from the components of the financial statements from both the balance sheet and the company’s profit and loss. This study offers a new method for measuring financial distress in companies with Artificial Neural Networks (ANN). The model provided comes from several financial ratios in 17 construction companies listed on the Indonesia Stock Exchange. The model is expected to produce the best model by showing the lowest prediction error rate. The results showed that the best ANN model has 25 inputs, 20 hidden layer neurons, and 1 best model output. The model obtained will be tested directly on the sample used; the results are that 6 construction companies in Indonesia have financial distress and 11 non-distress problems. This result proves that the best model obtained can predict the level of financial distress of companies with a small error rate to produce 6 companies identified as financially distressed. This result can be a warning for companies to increase revenue by adding new projects to get out of financial distress status. Traditional financial distress models such as Altman, Zmijewski, Springate, and Fulmer, which have become researchers’ guidelines for measuring financial distress, can be added to the ANN 25-20-1 model as a comparison to strengthen the research results.