Inflation Rate in the United States increased to 3 percent in September from 2.90 percent in August of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Dataset extracted from the post Understanding the Impact of Inflation on Mutual Fund Returns on Smart Investello.

Graph and download economic data for Inflation, consumer prices for the United States (FPCPITOTLZGUSA) from 1960 to 2024 about consumer, CPI, inflation, price index, indexes, price, and USA.

This panel dataset contains quarterly series on inflation targets, bands, and track records for 41 inflation targeting countries from 1990 to 2024. Data on inflation targets and bands are collected through each central bank’s historical documents and rules-based track record measures are calculated by the author to assess actual inflation outcomes with respect to the central banks’ stated policy objectives. The dataset supports research work in Zhang (2025), Zhang and Wang (2022), and Zhang (2021). Please cite the following paper when using the data: Z. Zhang, Inflation Targets, Bands, and Track Records: a Dataset of Inflation Targeting Countries, Data in Brief, Volume 61, 2025, 111753.

Other related papers: Z. Zhang, Does inflation targeting track record matter for asset prices? Evidence from stock, bond, and foreign exchange markets, Journal of International Financial Markets, Institutions and Money, Volume 101, 2025, 102141. Z. Zhang, S. Wang, Do actions speak louder than words? Assessing the effects of inflation targeting track records on macroeconomic performance, 2022, IMF Working Papers 2022/227.
Z. Zhang, Stock returns and inflation redux: An explanation from monetary policy in advanced and emerging markets, 2021, IMF Working Papers 2021/219.

The 2025 August online version has added two non-IT countries (Switzerland and China) for comparison purpose.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Inflation: Friend or Foe to the Stock Market?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This dataset contains monthly and quarterly time-series data from 2012 to 2024 for Indonesian sovereign credit risk (∆CDS), global volatility (VIX), international equity proxy (MSCI World Index), Indonesia Stock Exchange Composite Index (IHSG), exchange rate (USD/IDR), and inflation. The dataset supports the empirical analysis in the article titled “The Interaction Between Sovereign Risk, Global Volatility, and Domestic Stock Returns: An Indonesian Case Study.

Description Ashtead (“Sunbelt”) is the second largest equipment rental company in the States, and cyclical fears plus a few minor operational missteps have created an attractive entry point into a secular winner. I also believe Sunbelt is under-earning to a larger degree than peers because of the organic nature of recent growth. Business Overview I'll keep this short because this and other equipment rental companies have been covered on VIC. Sunbelt buys and maintains a fleet of equipment including aerial work platforms (30% of fleet), forklifts (20%), earthmoving (14%), power and HVAC (11%) and more. Equipment is depreciated over 10 years (chosen to make equipment disposals breakeven at the low point of a cycle) and Sunbelt typically keeps it around for 7 years, getting more than 50% of original cost ("OEC" or original equipment cost) in rental revenue per year. After 7 years, equipment is disposed of at 40 cents on the dollar. Non-resi construction end markets are less than half of the business, and the rest is industrial, MRO and more. Renting equipment lets you get the exact right piece of equipment for a job. As an example, you used to find backhoes on jobsites much more, because a backhoe is the swiss army knife of earthmoving. That user might now prefer to rent either an excavator or a bucket loader, each of which peform half the function of the backhoe but in a more efficient manner. Rental also conserves capital, reduces the need for equipment yards/storage, solves logistics/ eliminates the need for vehicles that can move equipment, and solves the difficulty of maintaining owned equipment. Secular Trends The secular tailwinds come from both increased rental penetration as well as market share gains by the largest players. The use of rented equipment accounts for about 55% of the equipment market today and I expect it to hit at least 65% over time. Penetration is up from the low 40% range pre-GFC and single-digits in the 1990s. The top two players URI and Sunbelt have 15% and 11% share, respectively, and players smaller than the top 100 have 44% of the market. The top 10 players have grown market share from 20% in 2010 to about 45% today. The largest rental company businesses have improved over time. Scale gives purchasing economies with OEM suppliers, efficiencies in logistics and maintenance, and higher equipment utilization. URI and Sunbelt purchase equipment 15-20% cheaper than mom & pop operators. Moving heavy equipment to and from job sites requires a large fleet of dedicated vehicles. Equipment maintenance benefits from having expertise by equipment type, mechanic sharing and better utilization of parts and spares. In a typical branch, 6 out of 20 total employees might be mechanics. Utilization is measured both by time/physical utilization, which is just the amount of time the equipment is on rent, or by dollar utilization, which is measured by the rental revenue divided by the cost of the equipment (basically, asset turns). Dollar utilization is perhaps the most important metric, because it combines the time on rent and the rental rate. Dollar utilization is higher at the scale players for a large variety of reasons. More locations give larger players density and a higher likelihood that a given piece of equipment is needed by someone in that geography. It also lowers transportation costs and time and most importantly allows locations to share equipment. A better repair function means machines are on rent for longer and means that there is more equipment available to rent. A wider variety of equipment on rent also leads to higher rates. Sunbelt frequently mentions that they are not the lowest price, but they win business because of breadth, availability and service. The factors I’ve outlined above have led to stable dollar utilization, rising margins and thus rising returns on capital over time: Specialty rental equipment has become a larger part of Sunbelt’s mix over time. Specialty is a catch-all for equipment that can have more of a service component or more of a temporary, emergency, or one-off use case. When looking at historical results, note that specialty carries lower physical utilization but higher margins. Specialty equipment also depreciates more slowly and is generally less cyclical than general tool (i.e. non-specialty). Cyclical Factors Equipment rental is a cyclical business. Sunbelt will tell you that because equipment rental is now an essential part of customer’s businesses, rather than used as a top-up, future cycles will be more muted than the past. I mostly believe this for a few reasons. First, the large players are larger and more sophisticated. CEO Brendan Horgan likes to say that in the GFC they almost blindly lowered prices by 20% across the board without any pricing tools or great reason to do so. Second, the top 10 players are less leveraged. In the GFC, you not only had more leveraged companies, but some companies actually had covenants tied to time utilization. You can imagine what incentives that creates. Leverage at all the large players has decreased steadily over time. Finally, 70% of the industry contributes and subscribes to Rouse data (owned by RB Global), which provides detailed rate and utilization data by equipment type and geography. This was not the case in the GFC, and even a decade ago large players including Sunbelt did not contribute. Cycles will still happen, but the cash flow characteristics of the business blunt the impact. Rental equipment is typically sold and replaced after seven years, and you can see below that in past downturns capex can effectively be turned off for a time even as aged equipment is still sold. Sunbelt has a young fleet, partly because organic growth necessitates it, and so aging the equipment a year by turning off capex can easily be done in future downturns. Replacing a seventh of your capital every year is actually helpful in downturns because not replacing it means that equilibrium can be reached faster versus having something like a factory running at low levels of utilization. [a note on Sunbelt's fleet age: historically Sunbelt weighted age by net book value, versus gross book value at URI and other US based peers, and this flattered Sunbelt. Sunbelt's fleet is still younger, but this is because they've grown by adding brand new fleet versus M&A/acquiring fleet as URI has done] It’s worth noting that in the Oil & Gas Downturn and during Covid that rates did not fall despite used equipment values falling. Historically this was not the case. Current Conditions The pandemic was characterized by a quick and steep decline in (all) business activity, followed by a scramble to get new equipment. Lead times doubled and tripled and large companies like Sunbelt found themselves waiting almost a year for new equipment, while smaller companies had trouble getting it at all. In recent quarters, equipment availability normalized and Ashtead found itself with slightly more equipment than it would have liked. Inflation is a double edged sword here. Rates need to keep pace with inflation to maintain returns, but Sunbelt and other large players are relatively better off than others. Equipment prices are 20%+ higher than pre-pandemic levels, and Sunbelt has replaced a lot of the fleet at these higher levels, whereas mom & pop players were not able to because of availability. Rates will benefit as these small players replace aged fleet with new fleet at significantly higher prices. Megaprojects, roughly defined as those projects with more than $400mm of value, provide additional opportunities and challenges. The trio of the Jobs Act, the IRA, and the CHIPs act have created a large backlog of megaprojects that will (probably) offset any weakness in commercial construction. Megaprojects favor the larger players. Sunbelt claims 30% market share in these projects, i.e. almost triple their national share. Only the largest players can serve these projects. Sunbelt has examples where they have over $100mm of fleet on a single project. Pandemic related shortages and megaprojects have contributed to recent disappointments in the stock. Both of these factors make it difficult to perfectly plan equipment needs, and ordering equipment early because you’re worried about availability or because you’re staging it for a megaproject can hurt utilization. I view these challenges as easily surmountable. Construction, which is 40% of the customer mix, is rate sensitive, and recently Sunbelt has seen customers delay projects as they wait for clarity on rates. Most of the slack has been taken up by megaprojects ramping. We’re coming off good times, so I think of mid-cycle as normalizing utilization and margin while also accounting for maturing greenfield locations. I think the most likely scenario in the near term is that softness in construction continues to be mostly offset by megaprojects driven by the desire to re-shore and fix our crumbling infrastructure. Valuation As I mentioned earlier, I believe Sunbelt is under-earning. Sunbelt has grown organically to a much larger degree than URI, and they’ve done it by putting new equipment in greenfield locations. These locations take a while to scale from both a fleet and margin perspective. Locations 10+ years old have 56% EBITDA margins. Locations 0-2 years old have 46% margins and locations 2-5 years old have 53% margins. If you apply this to the current store base, mature margins would be three points higher. Margins will also be helped by what Sunbelt calls “cluster economics,” which is just increasing density in markets. Clustered markets carry a few more points of margin and return. I value the business by assuming continued rental penetration, further share gains, and higher returns/margins (note below that I have market share at 13%, but recently an industry publication changed their methodology to include more specialty lines in the market definition and thus share is now

Context

The stock market has consistently proven to be a good place to invest in and save for the future. There are a lot of compelling reasons to invest in stocks. It can help in fighting inflation, create wealth, and also provides some tax benefits. Good steady returns on investments over a long period of time can also grow a lot more than seems possible. Also, thanks to the power of compound interest, the earlier one starts investing, the larger the corpus one can have for retirement. Overall, investing in stocks can help meet life's financial aspirations.

It is important to maintain a diversified portfolio when investing in stocks in order to maximise earnings under any market condition. Having a diversified portfolio tends to yield higher returns and face lower risk by tempering potential losses when the market is down. It is often easy to get lost in a sea of financial metrics to analyze while determining the worth of a stock, and doing the same for a multitude of stocks to identify the right picks for an individual can be a tedious task. By doing a cluster analysis, one can identify stocks that exhibit similar characteristics and ones which exhibit minimum correlation. This will help investors better analyze stocks across different market segments and help protect against risks that could make the portfolio vulnerable to losses.

Objective

Trade&Ahead is a financial consultancy firm who provide their customers with personalized investment strategies. They have hired you as a Data Scientist and provided you with data comprising stock price and some financial indicators for a few companies listed under the New York Stock Exchange. They have assigned you the tasks of analyzing the data, grouping the stocks based on the attributes provided, and sharing insights about the characteristics of each group

Data Dictionary

Ticker Symbol: An abbreviation used to uniquely identify publicly traded shares of a particular stock on a particular stock market Company: Name of the company GICS Sector: The specific economic sector assigned to a company by the Global Industry Classification Standard (GICS) that best defines its business operations GICS Sub Industry: The specific sub-industry group assigned to a company by the Global Industry Classification Standard (GICS) that best defines its business operations Current Price: Current stock price in dollars Price Change: Percentage change in the stock price in 13 weeks Volatility: Standard deviation of the stock price over the past 13 weeks ROE: A measure of financial performance calculated by dividing net income by shareholders' equity (shareholders' equity is equal to a company's assets minus its debt) Cash Ratio: The ratio of a company's total reserves of cash and cash equivalents to its total current liabilities Net Cash Flow: The difference between a company's cash inflows and outflows (in dollars) Net Income: Revenues minus expenses, interest, and taxes (in dollars) Earnings Per Share: Company's net profit divided by the number of common shares it has outstanding (in dollars) Estimated Shares Outstanding: Company's stock currently held by all its shareholders P/E Ratio: Ratio of the company's current stock price to the earnings per share P/B Ratio: Ratio of the company's stock price per share by its book value per share (book value of a company is the net difference between that company's total assets and total liabilities)

Hybrid LCA database generated using ecoinvent3.5 and EXIOBASE3, i.e., each process of the original ecoinvent3.5 database is added new direct inputs (coming from EXIOBASE3) deemed missing (e.g., services). Each process of the resulting hybrid database is thus not (or at least less) truncated and the calculated lifecycle emissions/impacts should therefore be closer to reality.

For license reasons, only the added inputs for each process of ecoinvent are provided (and not all the inputs).

Why are there two versions?

One of the version corresponds to ecoinvent hybridized with the normal version of EXIOBASE and the other is hybridized with a capital-endogenized version of EXIOBASE3.

What does capital endogenization do?

It matches capital goods formation to the value chains of products where they are required. In a more LCA way of speaking, EXIOBASE in its normal version does not allocate capital use to value chains. It's like if ecoinvent processes had no inputs of buildings, etc. in their unit process inventory. For more detail on this, refer to (Södersten et al., 2019) or (Miller et al., 2019).

So which version do I use?

Using the version "with capitals" gives a more comprehensive coverage. Using the "without capitals" version means that if a process of ecoinvent misses inputs of capital goods (e.g., a process does not include the company laptops of the employees), it won't be added. It comes with its fair share of assumptions and uncertainties however.

How do I use the dataset?

First, to use it, you will need ecoinvent3.5 [cut-off] and EXIOBASE3 [product x product, year 2011]. In the two datasets of this package, only added inputs are given (i.e. inputs from EXIOBASE added to ecoinvent processes). Ecoinvent and EXIOBASE processes/sectors are not included, for copyright issues. You thus need both ecoinvent and EXIOBASE to calculate life cycle emissions/impacts.

Module to get ecoinvent in a Python format: https://github.com/majeau-bettez/ecospold2matrix (make sure to take the most up-to-date branch)

Module to get EXIOBASE in a Python format: https://github.com/konstantinstadler/pymrio (can also be installed with pip)

If you want to use the "with capitals" version of the hybrid database, you also need to use the capital endogenized version of EXIOBASE, available here: https://zenodo.org/record/3874309. Choose the pxp version of the year you plan to study (which should match with the year of the EXIOBASE version). You then need to normalize the capital matrix (i.e., divide by the total output x of EXIOBASE). Then, you simply add the normalized capital matrix (K) to the technology matrix (A) of EXIOBASE (see equation below).

Once you have all the data needed, you just need to apply a slightly modified version of the Leontief equation:

\(\begin{equation} \textbf{q}^{hyb} = \begin{bmatrix} \textbf{C}^{lca}\cdot\textbf{S}^{lca} & \textbf{C}^{io}\cdot\textbf{S}^{io} \end{bmatrix} \cdot \left( \textbf{I} - \begin{bmatrix} \textbf{A}^{lca} & \textbf{C}^{d} \\ \textbf{C}^{u} & \textbf{A}^{io}+\textbf{K}^{io} \end{bmatrix} \right) ^{-1} \cdot \left( \begin{bmatrix} \textbf{y}^{lca} \\ 0 \end{bmatrix} \right) \end{equation}\)

q^hyb gives the hybridized impact, i.e., the impacts of each process including the impacts generated by their new inputs.

C^lca and C^io are the respective characterization matrices for ecoinvent and EXIOBASE.

S^lca and S^io are the respective environmental extension matrices (or elementary flows in LCA terms) for ecoinvent and EXIOBASE.

I is the identity matrix.

A^lca and A^io are the respective technology matrices for ecoinvent and EXIOBASE (the ones loaded with ecospold2matrix and pymrio).

K^io is the capital matrix. If you do not use the endogenized version, do not include this matrix in the calculation.

C^u (or upstream cut-offs) is the matrix that you get in this dataset.

C^d (or downstream cut-offs) is simply a matrix of zeros in the case of this application.

Finally you define your final demand (or functional unit/set of functional units for LCA) as y^lca.

Can I use it with different versions of ecoinvent?

No it can only work with ecoinvent3.5. Unfortunately ecoinvent changes the UUIDs of their process between each version and also introduces additional processes.

Can I use it with different versions/reference years of EXIOBASE?

Technically speaking, yes it will work, because the temporal aspect does not intervene in the determination of the hybrid database presented here. However, keep in mind that there might be some inconsistencies. For example, you would need to multiply each of the inputs of the datasets by a factor to account for inflation. Prices of ecoinvent are defined in €2005. The inputs were calculated for the year 2011 using a inflation factor of 1.13.

How do I link UUIDs of ecoinvent to metadata?

Ecospold2matrix stores all the metadata into the PRO matrix.

Why is the equation (I-A)^-1 and not A^-1 like in LCA?

IO and LCA have the same computational background. In LCA however, the convention is to represents outputs and inputs in the technology matrix. That's why there is a diagonal of 1s (the outputs, i.e. functional units) and negative values elsewhere (inputs). In IO, the technology matrix does not include outputs and only registers inputs as positive values. In the end, it is just a convention difference. If we call T the technology matrix of LCA and A the technology matrix of IO we have T = I-A. When you load ecoinvent using ecospold2matrix, the resulting version of ecoinvent will already be in IO convention and you won't have to bother with it.

Pymrio does not provide a characterization matrix for EXIOBASE, what do I do?

You can find an up-to-date characterization matrix (with Impact World+) for environmental extensions of EXIOBASE here: https://zenodo.org/record/3890339

If you want to match characterization across both EXIOBASE and ecoinvent (which you should do), here you can find a characterization matrix with Impact World+ for ecoinvent: https://zenodo.org/record/3890367

It's too complicated...

The custom software that was used to develop these datasets already deals with some of the steps described. Go check it out: https://github.com/MaximeAgez/pylcaio. You can also generate your own hybrid version of ecoinvent3.5 using this software (you can play with some parameters like correction for double counting, inflation rate, change price data to be used, etc.). As of pylcaio v2.1, the resulting hybrid database (generated directly by pylcaio) can be exported and manipulated to brightway2.

Where can I get more information?

The whole methodology is detailed in (Agez et al., 2021).

If needed, I will be happy to help: maxime.agez@polymtl.ca

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

The benchmark interest rate in Pakistan was last recorded at 11 percent. This dataset provides - Pakistan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Gold fell to 4,199.97 USD/t.oz on December 2, 2025, down 0.75% from the previous day. Over the past month, Gold's price has risen 4.93%, and is up 58.92% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on December of 2025.

The yield on 10 Year TIPS Yield rose to 1.83% on December 1, 2025, marking a 0.07 percentage points increase from the previous session. Over the past month, the yield has edged up by 0.03 points, though it remains 0.10 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. This dataset includes a chart with historical data for the United States 10 Year TIPS Yield.

Argentina's main stock market index, the Merval, rose to 3060289 points on December 1, 2025, gaining 1.12% from the previous session. Over the past month, the index has declined 1.42%, though it remains 33.32% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Argentina. Argentina Stock Market (MERVAL) - values, historical data, forecasts and news - updated on December of 2025.

The USD/JPY exchange rate rose to 155.6000 on December 2, 2025, up 0.09% from the previous session. Over the past month, the Japanese Yen has weakened 0.90%, and is down by 4.00% over the last 12 months. Japanese Yen - values, historical data, forecasts and news - updated on December of 2025.

Turkey's main stock market index, the BIST 100, rose to 11132 points on December 2, 2025, gaining 0.14% from the previous session. Over the past month, the index has climbed 0.64% and is up 13.27% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Turkey. Turkey Stock Market - values, historical data, forecasts and news - updated on December of 2025.

Brazil's main stock market index, the IBOVESPA, rose to 159976 points on December 2, 2025, gaining 0.86% from the previous session. Over the past month, the index has climbed 6.33% and is up 26.83% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Brazil. Brazil Stock Market (BOVESPA) - values, historical data, forecasts and news - updated on December of 2025.

Canada's main stock market index, the TSX, fell to 30943 points on December 2, 2025, losing 0.51% from the previous session. Over the past month, the index has climbed 2.21% and is up 20.70% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Canada. Canada Stock Market Index (TSX) - values, historical data, forecasts and news - updated on December of 2025.