Impact of Technical Indicators and Leading Indicators on Stock Trends on the Internet of Things

Table 1

Prediction methods, input features, and model types.

Methods		Data				Target
		HD^1	TI^2	F/O^3	OFD^4	2-class	3-classs	Regression
#1 [32]	SVM, NN		√			√
#2 [33]	RF, SVM, KF, Adaboost, NN, KNN, LR				√	√
#3 [34]	WNN + GA	√						√
#4 [35]	Fractal dimension + ACO + SVM		√			√
#5 [36]	SVM + SVM		√			√
#6 [37]	HS + NN, GA + NN		√					√
#7 [38]	GA + XGBoost	√	√			√
#8 [39]	2D CNN	√	√			√
#9 [40]	2D CNN, 3D CNN	√	√			√
#10 [41]	CNN, LSTM, MLP, RF	√					√
#11 [42]	2D CNN	√		√			√
#12 [43]	LSTM	√		√			√
#13 [44]	CNN + LSTM	√		√			√
#14 [45]	NTN				√	√
#15 [46]	Attention LSTM + transformer	√				√

¹HD: historical data. Basic stock price information, including opening price, the highest price, the lowest price, closing price, and volume. ²TI: technical indicators. It refers to a collection of stock data calculated by mathematical formulas. ³F/O: futures/options. Information about futures contracts and options contracts for stocks. ⁴OFD: other financial data. Information beyond the stock itself—GDP, unemployment rate, other macroeconomic indicators, crude oil prices, gold prices, information about other stocks related to this stock, financial news, and so on.