Stock Market Forecasting: Key to Risk Management and Trader Success - Essay Sample

Introduction

Stock markets are often unpredictable; therefore, historical stock data is critical when modeling structure to predict the future prices. Developing and monitoring an investment portfolio is vital to a trader's success to make decent returns (Horlach, Schirmer & Drews, 2019). The associated research on stock price forecasting is based on either buying or selling of stock (Jiang & Zhang, 2019). Stork market forecasting is an effective tool for risk management and plays a critical role in portfolio diversification. Various forecasting techniques exist, however, this paper will apply techniques including simple moving average, weighted moving average, and simple exponential smoothing methods. Data were obtained from Yahoo Finance, primarily focusing on the New Zealand stock exchange market (Yahoo Finance, 2020). The companies selected are from different industries to develop a distinctive portfolio. The three companies selected for analysis include:

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• Auckland International Airport Limited (AIA NZ)
• Spark New Zealand Limited (SPK.NZ)
• Port of Tauranga Limited (NS) (POT.NZ)

The data obtained for analysis were closing stock prices of between May-10-2017 and May-9-2020. An alpha of 0.7 was used to calculate the exponential smoothing.

Findings

The analysis established a simple moving average is smooth with the initial closing stock prices, reducing the volatility of the portfolio. The short-term transitions of the daily stock makes the moving average more volatile; however, the predicted value was close to the actual value. The simple moving average is critical when identifying a trend that is on an upward or downward trend to inform an investor on whether to buy or sell the stock (Reuters, 2019). The simple moving average for Auckland International Airport Limited was predictive since price fluctuations were successfully smoothened out. Nevertheless, simple moving averages do not offer clear direction of whether or not more attention ought to be placed on the last days of the given period.

The weighted moving average was computed using the excel data analysis tool pack. The estimates of the Weighted moving average was close to the actual price, hence, yielding a much smoother trend that blended with the actual value graph. The weights increase and decrease slowly, producing a smooth curve compared to the simple moving average.

In the Single Exponential Smoothing (SES), an alpha of 0.7 was used in all the stocks. A more significant smoothing factor was opted for the analysis to control the influence of the previous day's closing stock and primarily focus on the recent past observations. NZHerald (2019) indicates that alpha close to 1 shows that most of the data considered for the forecasting model gives more attention to the most recent past observations, while values close to 1 account for more historical prices when creating a prediction model. The three-point weighted and straightforward moving averages gave a precise forecast of the AIA.NZ daily prices, figure 1 and 2 shows clear smooths. However, despite using a high Alpha (0.7), the exponential smoothing method was not accurate, especially when prices suddenly fall or rise. The forecasts did not follow the flow of the actual data in the AIA.NZ stock series, therefore, it is not reliable for investors to use this technique when there are trends in before the focusing. For instance, on 25th May 2020, when the stock prices hit the lowest point, the simple moving average and the single exponential smoothing, the predictions are likely to be incorrect since they are primarily based on the latest estimates of the closing price.

According to NZX (2020) investors often utilize forecasting information to establish occasions that affect the price of a accompany, including the expectation of sales, rise or fall of share prices of a company or a pandemic such as Coronavirus (COVID-19) that affected the stock prices of the companies analyzed. The ongoing COVID-19 pandemic resulted in significant economic disruptions in New Zealand that affected all sectors of the economy (Stats.govt. 2020.

Conclusion

Forecasting in the stock market is vital since it helps investors/bondholders make informed decisions when developing a portfolio, and whether to buy or sell a stock at a particular time (Rabelo, 2019. The analysis shows that simple moving averages and weighted moving averages fit the data for the stocks analyzed. However, the smoothing is the trends when stock prices sharply rise or fall.

References

Horlach, B., Schirmer, I., & Drews, P. (2019). Agile Portfolio Management: Design Goals and Principles. In Proceedings of the European Conference on Information Systems, Stockholm-Uppsala, Sweden (ECIS 2019). AIS Electronic Library (AISeL). doi:10.100710799-017-0285-9

Jiang, X. Q., & Zhang, L. C. (2019). Stock price fluctuation prediction method based on time series analysis. Discrete & Continuous Dynamical Systems-S, 12(4&5), 915-927. doi: 10.3934/dcdss.2019061

NZHerald (2019). Air NZ share price slides 14 pct after earnings downgrade. Retrieved from https://www.nzherald.co.nz/business/news/article.cfm?c_id=3&objectid=12198396

NZX (2020). New Zealand's Main Exchange (NZSX) Main Boar. Retrieved from https://www.nzx.com/markets/NZSX

Rabelo, L. (2019, July 31). Forecasting Prices for Many Days Ahead. Retrieved from https://towardsdatascience.com/forecasting-prices-for-5-days-ahead-2460406c4ea2

Reuters (2019). BRIEF-Auckland International Airport Updates On Government Advice Regarding Domestic Travel. Retrieved from https://www.reuters.com/article/brief-auckland-international-airport-upd/brief-auckland-international-airport-updates-on-government-advice-regarding-domestic-travel-idUSFWN2B80FD

Stats.govt. (2020). Economic impact of COVID-19 at a glance: Stats NZ. Retrieved from https://www.stats.govt.nz/news/economic-impact-of-covid-19-at-a-glance

Yahoo Finance (2020). New Zealand Stock Market Data. Retrieved from https://nz.finance.yahoo.com/