论文标题
通过股票市场指标进行住房预测
Housing Forecasts via Stock Market Indicators
论文作者
论文摘要
通过将住房数据重新解释为烛台，我们将Liang和Unwin [Lu22]的文章扩展到了COVID-19数据的股票市场指标，并利用股票市场中一些最杰出的技术指标来估算房屋市场的未来变化，并将发现与研究结果相比，从研究结果中可以从研究中获得研究。通过对MACD，RSI和烛台指标进行分析（Bullish吞没，吞噬，悬挂的人和Hammer），我们在对美国数据集（使用Zillow Housing数据）进行预测（使用Zillow Housing数据）方面表现出统计学意义，并考虑了它们在三种不同的风景中的应用：一个稳定的房地产市场，一个稳定的住房市场，一个稳定的房屋市场，一个稳定的房屋市场，一个饱和的市场和饱满的市场。特别是，我们表明看跌指标的统计意义要比看涨的指标更高，并且我们进一步说明了如何在较不稳定或人口较多的国家中，与看见的趋势相比，看跌趋势的统计学效果仅略有略有统计学。
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.
