Singapore's main stock market index, the STI, fell to 4298 points on September 9, 2025, losing 0.25% from the previous session. Over the past month, the index has climbed 1.53% and is up 22.34% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Singapore. Singapore Stock Market (STI) - values, historical data, forecasts and news - updated on September of 2025.

Prices for FTSE Straits Times Index including live quotes, historical charts and news. FTSE Straits Times Index was last updated by Trading Economics this August 30 of 2025.

Key information about Singapore FTSE Strait Times

• Singapore FTSE Strait Times closed at 3,895.7 points in Feb 2025, compared with 3,855.8 points at the previous month end
• Singapore Equity Market Index: Month End: FTSE Strait Times data is updated monthly, available from Jan 1985 to Feb 2025, with an average number of 2,122.9 points
• The data reached an all-time high of 3,895.7 points in Feb 2025 and a record low of 465.1 points in Apr 1986

• In the latest reports, FTSE All Share recorded a monthly P/E ratio of 13.2 in Jun 2022

A dataset of key technical indicators for STI Index, including RSI and MACD, used for technical analysis.

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Singapore FTSE: ST: Index: Month End: All-Share (AS) data was reported at 783.198 Index in Mar 2019. This records an increase from the previous number of 781.139 Index for Feb 2019. Singapore FTSE: ST: Index: Month End: All-Share (AS) data is updated monthly, averaging 755.526 Index from Jan 2008 (Median) to Mar 2019, with 135 observations. The data reached an all-time high of 868.018 Index in Apr 2018 and a record low of 381.600 Index in Feb 2009. Singapore FTSE: ST: Index: Month End: All-Share (AS) data remains active status in CEIC and is reported by FTSE. The data is categorized under Global Database’s Singapore – Table SG.Z005: FTSE: Straits Times (ST): Index.

In April 2025, the China Maritime Silk Road Trade Index (STI) showed that the import and export trade indices to countries along the 'Belt and Road' project were ****** and ****** points, respectively. The STI 'Belt and Road' export index increased by more than *** percent over the previous month, while the STI 'Belt and Road' import index declined by almost ** percent over the previous month and by almost *** percent year-on-year.

This data series of stock market indices includes FTSE 100(FTSE), AEX Index(AEX), DAX(GDAXI) and Straits Times Index(STI), from January 2007 to December 2017. And all these data is from a third party, downloaded with R software from 'Yahoo finance'.

Key information about Singapore P/E ratio

• Singapore FTSE All Share recorded a monthly P/E ratio of 13.159 on Jun 2023, compared with 13.824 from the previous month.
• Singapore FTSE All Share P/E ratio is updated monthly, with historical data available from Apr 1998 to Jun 2022.
• The P/E ratio reached an all-time high of 99.190 in Dec 1999 and a record low of 5.710 in Feb 2009.
• CEIC extends history for monthly P/E Ratio. FTSE Russell provides monthly P/E Ratio. P/E Ratio prior to Nov 2012 is sourced from Singapore Exchange.

In May 2022, the China Maritime Silk Road Trade Index (STI) showed an import trade index of ****** points. This represents an increase of about *** percent over the previous month and *** percent over the same month of the previous year.

This dataset contains the Singapore Stock Market Index (STI) and Standard & Poor's Composite 500 Index (S&P500), from January 1, 2000 to December 31, 2015.

In May 2022, the Maritime Silk Road Trade Index (STI) showed that China's composite trade index for countries along the 'Belt and Road' project was ****** points, an increase of about *** percent over the previous month and **** percent from the same period of the previous year.

Turkey Service Turnover Index (STI): NACE 2: Working Day Adjusted (wda) data was reported at 156.388 2015=100 in May 2018. This records an increase from the previous number of 146.857 2015=100 for Apr 2018. Turkey Service Turnover Index (STI): NACE 2: Working Day Adjusted (wda) data is updated monthly, averaging 78.255 2015=100 from Jan 2009 (Median) to May 2018, with 113 observations. The data reached an all-time high of 186.258 2015=100 in Dec 2017 and a record low of 31.954 2015=100 in Feb 2009. Turkey Service Turnover Index (STI): NACE 2: Working Day Adjusted (wda) data remains active status in CEIC and is reported by Turkish Statistical Institute. The data is categorized under Global Database’s Turkey – Table TR.H017: Service Turnover Index: 2015=100: Working Day Adjusted.

Uncover historical ownership history and changes over time by performing a reverse Whois lookup for the company STI.

In recent years, persistent homology (PH) and topological data analysis (TDA) have gained increasing attention in the fields of shape recognition, image analysis, data analysis, machine learning, computer vision, computational biology, brain functional networks, financial networks, haze detection, etc. In this article, we will focus on stock markets and demonstrate how TDA can be useful in this regard. We first explain signatures that can be detected using TDA, for three toy models of topological changes. We then showed how to go beyond network concepts like nodes (0-simplex) and links (1-simplex), and the standard minimal spanning tree or planar maximally filtered graph picture of the cross correlations in stock markets, to work with faces (2-simplex) or any k-dim simplex in TDA. By scanning through a full range of correlation thresholds in a procedure called filtration, we were able to examine robust topological features (i.e. less susceptible to random noise) in higher dimensions. To demonstrate the advantages of TDA, we collected time-series data from the Straits Times Index and Taiwan Capitalization Weighted Stock Index (TAIEX), and then computed barcodes, persistence diagrams, persistent entropy, the bottleneck distance, Betti numbers, and Euler characteristic. We found that during the periods of market crashes, the homology groups become less persistent as we vary the characteristic correlation. For both markets, we found consistent signatures associated with market crashes in the Betti numbers, Euler characteristics, and persistent entropy, in agreement with our theoretical expectations.

Based on the coupling and interaction relationship between China’s cultural industry (CI) and scientific & technological innovation (STI), this study constructed an index system for their coordinated development. The weight of each indicator was determined by using the entropy value method (EVM), and the coupling coordination degree (CCD) model was used to calculate CCD and coordination degree of China’s CI and STI from 2012 to 2020. On this basis, the key factors in the coupling effect were analyzed using grey correlation degree (GCD). The results demonstrate that: (1) there is a high-level coupling relationship between China’s CI and technological innovation; (2) the level of coupling coordination between the two is generally on the rise, experiencing a development process from serious maladjustment to high coordination; (3) Industry resources, policy support and the cost of cultural undertakings are the endogenous factors restricting the development of CI, and the environment and output of STI are the key factors restricting the coupling and coordinated development of Chinese CI and STI.

Turkey Service Turnover Index (STI): NACE 2 data was reported at 158.945 2015=100 in May 2018. This records an increase from the previous number of 146.996 2015=100 for Apr 2018. Turkey Service Turnover Index (STI): NACE 2 data is updated monthly, averaging 78.397 2015=100 from Jan 2009 (Median) to May 2018, with 113 observations. The data reached an all-time high of 185.618 2015=100 in Dec 2017 and a record low of 32.078 2015=100 in Feb 2009. Turkey Service Turnover Index (STI): NACE 2 data remains active status in CEIC and is reported by Turkish Statistical Institute. The data is categorized under Global Database’s Turkey – Table TR.H016: Service Turnover Index: 2015=100.

The Baijiu industry is a significant contributor to both the food industry and the light industry. Its high tax characteristics effectively promote the sustainable development of the regional economy. First, the evaluation index system of scientific and technological innovation (STI) and high-quality development of Baijiu industry (HQDBI) were constructed. The entropy-improved CRITIC method was used to measure the weights. Second, the coordination relationship and evolution trend of STI and HQDBI were explored using the coupling coordination model and the Tapio decoupling model. Then, the transfer law and key influencing factors were further investigated using the Markov chain and grey correlation, respectively. The main contribution is the dynamic evolution of the coupling and decoupling relationships from the perspective of multiple Baijiu provinces, and deeply depicts the coordination relationship and evolutionary trends of STI and HQDBI. The results show that: the spatial distribution of the coupling coordination degree shows high values in the east-west and low values in the north-south characteristics. In 2021, a pattern of coordinated development in Baijiu provinces has emerged along the Yangtze River basin. The decoupling state is mainly strong decoupling, but it remains poor in Shanxi. The coordination process is unstable and difficult to achieve leapfrog development. Coordination, sustainability and innovation environment have a greater impact on the coordination of subsystems.

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The species temperature index (STI), which is the mean temperature of the distribution area for bird species, has been calculated to evaluate the response of birds to climate change. In Japan, the STI for the breeding season has been calculated based on the results of the “Japan Breeding Bird Atlas.” Since the effects of climate change are more pronounced during the wintering season of birds (Lehikoinen et al. 2021), it is important to know the STI for the wintering season in Japan. We surveyed to determine the wintering distribution of birds in Japan between January 2016 and February 2022. However, the 40 km grid to collect distribution information used in this survey included areas of low to high elevation in the steep terrain of Japan which made the temperature differences within the grid too large to show STI. Since the Japanese archipelago extends from southwest to northeast, the distribution of any bird species in the Japanese archipelago can be indicated by the value obtained by adding latitude and longitude. Therefore, we calculated ”Latitude-Longitude Index” values that describe the distribution of various bird species during the wintering season in Japan to replace the STI. Here we present this method and propose it is a useful approach for studying the effects of climate change on birds.