1. Introduction
In recent decades, innovation expression has taken special importance in all fields. Innovations facilitate the emergence of globalized markets, where every participant has the advantage of being connected regardless of distance, and the world comes together in a way commonly described as a small village. This contemporary village requires its participants to acquire the innovation and competitive advantages once they have both the ability and desire to converge distances between them; otherwise, survival could be imaginary. Hereafter, innovation has a concentrated share of organizational structure, company existence, and sustainability [
1].
According to Chesbrough [
2], the open innovation (OI) concept can be defined as exchanging ideas, knowledge, expertise, and technologies through inflows and outflows of communications that will shape a competitive advantage and lead to high performance. Adapting an open innovation technique may positively affect sales, cost reductions, and market advantages [
3] as well as firm stock values [
4]. Additionally, Huang and Chou [
5] used Tobin’s Q to measure the effect of open innovation (OI) on the performance of technological manufacturing firms. Moreover, Szutowski [
6] covered 398 open innovation announcements, released by European service companies, and found significant positive effects of OI disclosure on enterprise market values.
The circulation of OI, complex adaptive systems, and evolutionary change dynamics all together give the definition of OI based on a micro level [
7]. On the other hand, closed, opened, and social open innovations are the main components of OI once macrodynamics are taken into consideration [
8,
9]. Strengthening OI macrodynamics enhances the performance of entrepreneurs worldwide, which will support any coming industrial revolution [
10]. Yun and Liu [
1] found that the interaction of both knowledge sharing and resources may come with realizable sustainability that will last for the long run with a positive effect on the economy, the environment, and society, and this is the ultimate goal of applying macro- and microdynamics of OI with the quadruple helix model (QH) model.
The quadruple helix model (QH) comes as an improvement and extension to the triple helix model by adding the dimension of civil society, in which it is the interaction between governments, industries, and universities that bring about a more sustainable and creative climate [
11]. OI and social innovation become widely globalized at the economy level, letting leading companies lock down traditional innovation and move quickly toward open innovation, in which new markets have been pursued. While the triple helix serves developed countries efficiently, adding the fourth dimension of the link to society would work for both developed and emerging countries [
1]. As all real and financial markets are interrelated, the idea of estimating one financial market index using other financial market indices can be logically accepted.
It is not an easy mission to make a proper estimate of stock indices. The reasons are that the interactions of multi-factors walk in line and across each other, which makes the goal of reaching a proper estimate truly challenging. Market economic factors, interaction between stock markets, and international variables are some instances of these concerned factors [
12]. Unsurprisingly, specific movements of market indices may influence investors to buy, sell, or hold, especially if these occurrences take place at the right time. Therefore, an accurate estimation of the coming index movement may help investors to perform better while taking the right investment decision [
13,
14,
15,
16,
17,
18].
Several researchers have focused on using different input variables (i.e., day, month, year, trading volume, etc.) to study their power of estimation on the stock market index [
19,
20,
21,
22,
23,
24,
25]. Other scholars have investigated the effect of different parameters on market indices and stock returns. Ryu [
26], who found strong evidence that the product market competition has a negative effect on a stock returns, had taken into consideration the US–Korea Free trade agreement. Additionally, some academics studied the effect of specific events on market returns, such as Dichtl and Drobetz [
27], who found that Halloween has no effect on stock market movements of different countries (i.e., United States, Europe, Germany, France, and the United Kingdom).
Fang, Gozgor, Lau, et al. [
28] argued that investor sentiments (such as the Baidu index) could enhance stock market forecasting in China. Others focused on the role of publicly disclosed information, such as Heston and Sinha [
29], who used a neural network for 900,000 news stories to investigate their effects on predicting stock index returns. The results found that daily news could be used to predict up to a maximum of 2 days ahead, where weekly news can hold a periodic prediction power. Moreover, social media information could affect the stock market index, as shown in [
30,
31].
Authors have demonstrated the ability of using social media tweets to affect stock market index movements. It is important to mention that Hirshleifer and Shumway [
32] and Corby, Machado, and Fox [
33] took the climate factor under consideration to estimate the stock market index and found a strong relationship between weather conditions (i.e., sunny, windy, or raining) and stock market index prediction. Few scholars were successful in examining the relationship between one index and other indices and then forecasting the movement of this index [
34]. Other researchers have tried to estimate the relationship between a group of indices, such as Kapar, Olmo, and Ghalayini [
35], who investigated the financial integration between three stock indices (i.e., Dubai, Abu Dhabi, and the FTSE NASDAQ Dubai UAE 20 index) among the United Arab Emirates stock markets by using a vector error correction and permanent–transitory decomposition. The results revealed that UAE stock markets are integrated, so any shock to any market would have a strong effect on the other considered markets.
While the majority of the previously concerned published papers depend on macroeconomic factors (i.e., time, weather, etc.). However, few studies have discussed the relationship between international indices and the national stock market index [
34,
36]. Thus, the goal of this paper is to enrich the field of estimating Saudi stock market index based on specific international indices.
Dai and Zhou [
37] developed a new stock returns prediction model by combining sum-of-the-parts (SOP) method and three economic constraint methods, namely non-negative economic constraint strategy, momentum of return prediction strategy, and three-sigma strategy. The results showed that the model is robust and efficient. Moreover, to add realistic assumptions to the current stock, such as transaction costs, liquidity issues, and bid and spread basis, Meng and Khushi [
38] suggested using a reinforcement learning model in financial markets to enhance the current models in the field. Moreover, optimization algorithms are used with artificial intelligent prediction models to improve the efficiency of the predications in addition to enhancing the computation complexity [
39,
40]. Moreover, many methods in the literature suggested using different models to improve the prediction model of financial markets, namely neural network [
41] and Markov chain [
42] in addition to evaluating different machine learning models as discussed in [
43].
To the authors’ best knowledge, this is the first article to estimate the movement of the Saudi financial market (TADAWUL) index, commonly known as (TASI), by investigating the movements of selected key international indices. The main contributions of this paper are (i) an investigation of the relationship between six international stock indices (i.e., CAC40, DAX, NASDAQ, SP 500, NIKKEI 225, and HSI) and (TASI); (ii) determination of the effect of each international index on (TASI) using linear, logarithmic, quadratic, cubic, power, and exponential equations; and (iii) selection of the optimal equation that can efficiently describe the estimation of the Tadawul index (TASI).
The remainder of the paper is structured as follows.
Section 2 describes the International Stock Indices.
Section 3 discusses data collection and analysis. The research methodology of equation estimation models is shown in
Section 4 whereas the results that go along with analysis are explained in
Section 5. Finally,
Section 6 discusses the conclusions.
3. Tadawul Index (TASI) and International Indices
The Saudi stock exchange or Tadawul was established in March 2007 as the sole entity authorized in Kingdom of Saudi Arabia for stock trading. The Tadawul All Share Index (TASI) is the major stock market index which tracks the performance of all companies traded on Tadawul.
In fact, few scholars have discussed how to predict the Tadawul index (TASI) using some specific factors [
12,
45,
46,
47]. Alsufyani and Sarmidi [
48] found there is no relation between commodity energy price and TASI volatility. Some researchers have investigated other indices in the prediction of TASI, and Mohammed, Abdalhafid, and Ahmed [
34] studied the relationship between TASI and US stock market indices (S&P500 and Dow Jones) and found a relationship between Tadawul Market and US markets over the long run. In addition, Tissaoui and Azibi [
36] studied the predictability of Tadawul index and international indices and found that US volatility risk indices are dominant in forecasting the Saudi stock exchange. Moreover, researchers investigated the relationship between international indices and different variables, including the clean energy index [
49] and skew phenomenon [
50].
Moving to international indices, different countries have different general indices based on the strength of the market, for example, USA has three most popular stock indices including Dow Jones, S&P 500, and NASDAQ Composite. The stock market indices can be classified into many types based on the used criteria, whether global (i.e., S&P Global 100, MSCI World, etc.) or local (i.e., Tadawul index, Amman Exchange index, etc.). All global stock indices shared the same characteristics, for example, market indices describe a portfolio of investment holdings and follow the movement of the market segments. All stock market indices share the same methodology for constructing individual indices, since all calculations are considered based on weighted average mathematics.
In this study, only the most important indices from different countries, namely France, Germany, USA, Japan, Hong Kong, and Saudi Arabia, are taken into consideration. These indices will be employed to investigate the ability of estimating the movement of TASI in addition to using year, month, and day variables to show if the estimation of TASI index is possible or not. The selected indices used in this research are shown in
Table 1.
5. Results, Analysis and Discussion
To clarify the relationship between each independent variable (i.e., day, month, year, and international indices) and TASI stock index, we plot a graph for each variable
Figure 3 and
Figure 4. “Day” and “month” variables show that there are no linear relationships between these variables and the TASI index, where the “year” variable exhibits an incremental increase in the index until 2014, then a decrease for two years up to 2016 and return to increasing after 2016. The date pillar outputs express a limited relationship with day, month, and year standing apart from each other. However, unifying these three variables within the “time variable” could improve the estimation model.
Supporting the previously mentioned graphs results, a correlation test in
Table 3 is used to demonstrate the relations’ degree of the linearity model. This table presents high positive significant correlation coefficients among the concerned international indices. Day variable is not significant, month variable is insignificant except for a weak correlation with both TASI (negative direction) and NASDAQ (positive direction). Nevertheless, the year variable is positively correlated with all indices and with a high degree. This may indicate that all international indices demonstrate an additive effect on a year-to-year basis. In addition, as shown in
Table 3, all international indices (i.e., NASDAQ, SP500, NIKKEI, DAX, CAC40, and HSI) have a significant correlation with TASI, with correlation coefficients close to 0.5. Moreover, CAC40 and HSI express stronger correlations with the rest of indices compared to other variables. According to what has been mentioned, the results point out that international stock indices are proper estimators to forecast the movement style of the TASI index.
The relationship between TASI and international indices show that all indices have a linear relationship with TASI index (
Figure 3 and
Figure 4). In order to estimate the movement of TASI, the following steps have been applied: first, use day, month, and year variables together as the “time” variable. Second, each index of international indices with different estimating equations (
Section 3) was used separately in order to evaluate their performance on TASI (
Table 4). Third, the optimal model was selected as that with the best R
2, error, and F-values.
As shown above (
Table 4), the results reveal that the results of all deployed equations are significant according to R
2, error, and F-values. Hence, equations with different international indices and time variables are efficient and will possibly estimate TASI’s behavior. The overall top model is the power equation, in which the best R
2, error, and F-values have been obtained. In addition, time variable shows the highest performance, where the power equation is used to forecast TASI values. In
Table 4, the best model is HSI power equation as it has the best R
2, error, and F-values with 1, 0, and 358,601, respectively. This indicates that international indices are able to give an initial description of TASI’s expected movement; any changes in these indices due, e.g., to global variations, will in turn affect TASI.
ARIMA Model
After constructing equations that aim to estimate TASI using only one of the international indices as shown in
Table 4, we go in depth to investigate more complicated linear models by using the ARIMA model with the concerned international indices in addition, to day, month, and year variables. The study uses ARIMA model with Expert Modeler to choose the most significant variables that could be used to build a prediction model that can estimate TASI index. The results reveal that the best ARIMA model can be formulized as ARIMA (0,1,14).
According to
Table 5 and
Table 6, ARIMA results show that SP500, NIKKEI, CAC40, and HSI indices are the most appropriate variables to estimate TASI with R
2 and RMSE equal to 0.993 and 113, respectively. All these four indices have a positive effect on estimating TASI. SP500 has the strongest effect on forecasting TASI with an estimate coefficient of 0.366, followed by CAC40 then HSI and, finally, NIKKEI with 0.05. The relationship between estimated and observed model using ARIMA is shown in
Figure 4. In addition, the Ljung–Box test showed that the model is not significant with a
p-value equal to 0.407, which indicates that there would not be any remaining residual structures in the ARIMA model that uses four predictors. In addition, the proposed model did not include NASDQ index, since Expert Modeler choses the index that is highly correlated with TASI and is less correlated with other independent variables.
Figure 5 shows that the observed and estimated lines are almost similar, which support the idea of using ARIMA to estimate TASI. The findings of this study demonstrate that TASI relates to international indices and any events happening within the scope of these indices can have a huge impact on the TASI index. This relationship can be used to understand the opening price of TASI based on the closing prices of these international indices. Therefore, investors who are interested in TASI’s stocks investments initially have to keep their eyes open on international indices, as they can estimate TASI movement with more accuracy and may apparently achieve more gains.
Therefore, investors can predict the opening price of TASI using the closing prices of international stock indices, which would help many investors to sell and buy stocks based on international trends, especially in a case of crisis of international indices. Finally, today’s performance of SP500, NIKKEI, CAC40, and HSI, will be tomorrow’s performance of TASI. As a result, we are advising those who are investing or contemplating investing in Tadawul to benefit from predicting this expected performance to achieve an abnormal gain over their investments.
6. Conclusions
Globalization can convert the whole world into small village. Everything is connected to each other in both internal and external markets, which makes the existence or sustainability of companies complicated. On this point, innovation will make the difference as it creates competitive advantage for each company, as all markets are interrelated to each other [
2]. Saudi organizations showed worthy practices that are expected to shape the way for new innovations. In 2017, the capital market authority, CMA, announced the establishment of the financial tech experimental permit (FinTech ExPermit), which will provide new financing methods for investors through creating equity crowd funding platform. In addition, Tadawul and NASDAQ have officially signed an agreement to transfer the technology of post-trading infrastructure which is expected to take place in the second half of 2020. Moreover, e-voting and crowdfunding systems are going to be used in several financial markets all over the world and will deploy block chain innovation. Due to these forthcoming open innovation techniques, the Saudi market will have a high probability of joining the global open innovation zone soon. Hence, authors have attempted to estimate the Tadawul index (TASI) using key international indices and open innovation.
Several researchers have focused on using different input variables (i.e., day, month, year, trading volume, etc.) to study the power of estimation on stock market index [
19,
20,
21,
22,
23,
24,
25]. Heston and Sinha [
29] used neural network to investigate their effect on predicting stock index returns. Few scholars were successful in examining the relationship between one index and other indices, then to forecast the movement of this index [
34]. Other researchers tried to estimate the relationship between a group of indices, like Kapar, Olmo, and Ghalayini [
35], who investigated the financial integration between three stock indices (i.e., Dubai, Abu Dhabi, and the FTSE NASDAQ Dubai UAE 20 index) among the United Arab Emirates Stock Markets by using a vector error correction and permanent–transitory decomposition. The results revealed that the UAE stock markets are integrated, so any shock to any market would have a strong effect on the other considered markets. Mohammed, Abdalhafid, and Ahmed [
34] studied the relationship between TASI and US stock market indices (S&P500 and Dow Jones) and found a relationship between Tadawul Market and the US markets in the long run. In addition, Tissaoui and Azibi [
36] studied the predictability of Tadawul index and international indices and found that US volatility risk indices are dominant in forecasting the Saudi stock exchange.
This paper has studied different international indices including CAC40, DAX, NASDAQ, SP 500, NIKKEI 225, and HSI in order to estimate the movement of Tadawul index (TASI). The paper deploys a time series of 12 years from 2008 until 2019 to ensure that the results do not happen accidently or arbitrarily, besides to ensure the reliability of all employed equations. The statistical analysis has been made within two parallel stages; in the first stage, different equations (i.e., linear, logarithmic, quadratic, cubic, power, and exponential equations) are run and then the best model is selected among all of them. On the other hand, the ARIMA model is used to specify the most critical indices among all of them that can estimate TASI more accurately. The results reveal that the power equation with different independent variables besides the time variable is most able to estimate TASI. This provides strong evidence that prediction models can be used to estimate TASI’s behavior. The ARIMA model (with Expert Modeler) coefficients are described as ARIMA (0,1,14). The results demonstrate that SP500, NIKKEI, CAC40, and HSI indices are the most appropriate variables to estimate TASI with R2 and RMSE equal to 0.993 and 113, respectively.