Housing Forecasts via Stock Market Indicators
Through the reinterpretation of housing data as candlesticks, we extend Nature Scientific Reports' article by Liang and Unwin [LU22] on stock market indicators for COVID-19 data, and utilize some of the most prominent technical indicators from the stock market to estimate future changes in the housing market, comparing the findings to those one would obtain from studying real estate ETF's. By providing an analysis of MACD, RSI, and Candlestick indicators (Bullish Engulfing, Bearish Engulfing, Hanging Man, and Hammer), we exhibit their statistical significance in making predictions for USA data sets (using Zillow Housing data) and also consider their applications within three different scenarios: a stable housing market, a volatile housing market, and a saturated market. In particular, we show that bearish indicators have a much higher statistical significance then bullish indicators, and we further illustrate how in less stable or more populated countries, bearish trends are only slightly more statistically present compared to bullish trends.
Code (1)
Similar Papers 제목 키워드 기반
COVID-19 Forecasts via Stock Market Indicators
Reliable short term forecasting can provide potentially lifesaving insights into logistical planning, and in particular, into the optimal allocation of resources such as hospital staff and equipment. By reinterpreting CO…
SARF: Enhancing Stock Market Prediction with Sentiment-Augmented Random Forest
Stock trend forecasting, a challenging problem in the financial domain, involves ex-tensive data and related indicators. Relying solely on empirical analysis often yields unsustainable and ineffective results. Machine le…
Sentiment AnalysisStock Market PredictionPopulation growth, interest rate, and housing tax in the transitional China
This paper combines and develops the models in Lastrapes (2002) and Mankiw & Weil (1989), which enables us to analyze the effects of interest rate and population growth shocks on housing price in one integrated framework…
Efficiency of the financial markets during the COVID-19 crisis: time-varying parameters of fractional stable dynamics
This paper investigates the impact of COVID-19 on financial markets. It focuses on the evolution of the market efficiency, using two efficiency indicators: the Hurst exponent and the memory parameter of a fractional L\'e…
A data-science-driven short-term analysis of Amazon, Apple, Google, and Microsoft stocks
In this paper, we implement a combination of technical analysis and machine/deep learning-based analysis to build a trend classification model. The goal of the paper is to apprehend short-term market movement, and incorp…